RPSEA Final Project Report...2016/10/19 · Document No. 12121.6301.03.Final, Revision A Subsea...

RPSEA

Final Project Report

Document No.: 12121.6301.03.Final

Revision A

Subsea Produced Water Sensor Development

RPSEA Contract No. 12121-6301-03

October 19, 2016

Principal Investigator:

Jianfeng Zhang

Clearview Subsea LLC

16223 Park Row Drive, Suite 175

Houston, TX 77084

Document No. 12121.6301.03.Final, Revision A

Subsea Produced Water Sensor Development Project Final Report

October 19, 2016 2

LEGAL NOTICE

This report was prepared by Clearview Subsea LLC as an account of work sponsored by the

Research Partnership to Secure Energy for America, RPSEA. Neither RPSEA members of

RPSEA, the National Energy Technology Laboratory, the U.S. Department of Energy, nor any

person acting on behalf of any of the entities:

a. MAKES ANY WARRANTY OR REPRESENTATION, EXPRESS OR IMPLIED WITH

RESPECT TO ACCURACY, COMPLETENESS, OR USEFULNESS OF THE INFORMATION

CONTAINED IN THIS DOCUMENT, OR THAT THE USE OF ANY INFORMATION,

APPARATUS, METHOD, OR PROCESS DISCLOSED IN THIS DOCUMENT MAY NOT

INFRINGE PRIVATELY OWNED RIGHTS, OR

b. ASSUMES ANY LIABILITY WITH RESPECT TO THE USE OF, OR FOR ANY AND ALL

DAMAGES RESULTING FROM THE USE OF, ANY INFORMATION, APPARATUS,

METHOD, OR PROCESS DISCLOSED IN THIS DOCUMENT.

THIS IS A FINAL REPORT. THE DATA, CALCULATIONS, INFORMATION, CONCLUSIONS, AND/OR

RECOMMENDATIONS REPORTED HEREIN ARE THE PROPERTY OF THE U.S. DEPARTMENT OF

ENERGY.

REFERENCE TO TRADE NAMES OR SPECIFIC COMMERCIAL PRODUCTS, COMMODITIES, OR

SERVICES IN THIS REPORT DOES NOT REPRESENT OR CONSTIITUTE AND ENDORSEMENT,

RECOMMENDATION, OR FAVORING BY RPSEA OR ITS CONTRACTORS OF THE SPECIFIC

COMMERCIAL PRODUCT, COMMODITY, OR SERVICE.



October 19, 2016 3

ABSTRACT

This document is the final report for RPSEA Project 12121-6301-03, Subsea Produced Water Sensor

Development. The project focused on developing subsea produced water discharge quality monitoring

sensors, principally for monitoring oil-in-water (OiW) content for regulatory compliance and process

monitoring, but with some inclusion of solids content monitoring where sensor technologies permitted.

Findings of the program are also applicable to subsea produced water re-injection operations where

high water quality specifications are required, as well as online produced water monitoring in topsides

and onshore systems. Subsea produced water treatment and disposal has the potential to provide

benefits of lower total system cost, reduced environmental risk and improved flow assurance, with

water quality monitors being a key technology gap for such systems.

Through bench-scale testing of sensor prototypes, the project progressed the technology readiness

levels (API RP 17N) for subsea produced water discharge quality monitoring sensors to TRL 6 for the light

scattering sensor from Digitrol, and TRL 3 for three other sensors, namely: a microscopic imaging sensor

from J.M. Canty, laser induced fluorescence sensor from ProAnalysis, and a confocal laser fluorescence

microscopy (CLFM) sensor from Clearview Subsea. CLFM is a new technology for produced water quality

monitoring, upon which the project performed a proof of concept study and bench-scale tested the first

prototype. Although commercially available for surface duty, the ProAnalysis and J.M. Canty units are

considered prototypes in respect of subsea applications a d a e affo ded the a o e T‘L s u de standard API RP 17N with a note of caution on several aspects, including that reliability tests such as

reliability growth tests, accelerated life tests, have been performed only by Digitrol to date. The Digitrol

unit has been system installed in one known instance and remains considered a full scale prototype.

Technical requirements for subsea produced water monitoring sensors were developed, with industry

input and regulatory agency feedback, based upon current regulations for surface discharge in the Gulf

of Mexico. Using the technical requirements, and other factors considered important for developing

subsea produced water quality sensors such as TRL, a technology gap analysis and ranking exercise was

conducted for selecting the existing technology sensors with the most potential for development.

To evaluate the effect upon sensor accuracy of water quality parameters important to subsea

applications (including water composition & physical characteristics, sensor fouling mitigation and

transient condition memory effects), bench scale testing of three sensors selected from the ranking

exercise and the new technology sensor with CLFM was conducted using a once through flow loop. Oil,

solids, gas, salt and chemicals were added to filtered seawater, which was heated or cooled to the

required temperature, to make the synthetic produced water that match the test parameters for

testing the sensors with. EPA Method 1664B analytical procedure and Infrared Analysis procedure, both

using hexane extraction of materials from water samples, were developed to provide reference values

to evaluate the sensors with.

The ranges of parameters used in the bench-scale testing were selected to represent the common

subsea conditions, and included some amount of extremes for testing the accuracy and robustness of

the sensors. For any single subsea development, the full ranges of the parameters may not necessarily

occur. The relative importance of the parameters for good performance of the sensors may also be

different between developments. When the results from the current project are used in the evaluation

of applicable produced water quality sensor technologies, it is important that the use is within the

context of development-specific requirements and key parameters.



October 19, 2016 4

The bench-scale testing results showed that deviations in OiW content of less than 15% (compared to

hexane extracted materials measured with Infrared Analysis, which were correlated to the

measurement by EPA Method B a d a e efe ed to as EPA Equivalents o were achievable in

some conditions, but none of the sensors was able to achieve this target for the full range of water

composition variations that were studied and sensor measurements of OiW were affected by various

parameters to varying degrees (compositional variations included amongst other parameters: oil

content and API gravity, temperature, salinity, solids content, gas bubbles and chemical content).

All sensors were able to achieve OiW measurements within 50% deviation of the EPA Equivalents for

some parameters, but each sensor had two or more parameters for which it had much larger than 50%

deviation. While sensors were affected differently by changes in water composition, most of the effects

were consistent with expectations related to the measurement principles, be this image analysis, light

scattering or fluorescence. Further study is required to better understand how certain water

characteristics, such as the presence of production chemicals, e.g. hydrate inhibitor, impact sensor

accuracy and also the reference methods used (EPA Method 1664B and Infrared Analysis).

The bench-scale testing results showed that the tested existing sensor technologies (from Digitrol, J.M.

Canty and ProAnalysis) were robust, had good and acceptable accuracies under test conditions similar to

those at which the instruments were calibrated, and had well-defined trends in respect of the changing

compositional effect (i.e. the parameter effects). O fouli g itigatio , Digit ol s h d od a i approach worked well with both manual fouling induced by oil soaking and that by grease brush-painted

on the optical window. All the other sensors coped well with the fouling by oil soaking, but not by grease

brush-painted. Both the Digitrol and J.M. Canty sensors showed some memory effect, but this looks to

be minor and may not be considered significant for regulatory compliance monitoring purpose. The

tests also confirmed the feasibility of Clearview Subsea s CLFM technology and its potential for further

development into a robust and accurate subsea produced water quality sensor.

Regarding further development of the tested sensors, improvements were identified for achieving

higher accuracy, reducing the impact of parameter effects, and meeting the conditions of subsea

applications. Plans were also developed for qualification, environment testing, system integration, field

testing, and commercialization. Reliability testing were not included in the current project, and is one of

the steps required prior to achieving TRL 4, except for the Digitrol sensor that was tested for subsea

application.

It was also identified that the criteria for evaluating and accepting online sensors for as a tool for

regulatory compliance is an important aspect to be addressed ahead of, or during, further development

and commercialization. In particular, further assessment should be given on the required accuracy of the

sensors, such as whether it is necessary to develop sensors to an accuracy within +/-15% of current

regulatory techniques for OiW monitoring, and where the time duration to define the accuracy on

should be in the order of hourly as in the current technical requirements or longer term to match the

compliance reporting requirements for daily and monthly values.

Acceptance of regulators on the use of online produced water quality sensors and the technical

requirements is also critical for the deployment of the sensors. It is recommended that the oil and gas

industry continue working with regulatory agencies and obtain guidance as regulations and protocols on

the use are developed.



October 19, 2016 5

SIGNATURE

Jianfeng Zhang

Principal Investigator, 12121-6301-03

Clearview Subsea, LLC

Signed: ____________________________

Date: ____________________________

October 19, 2016



October 19, 2016 6

THIS PAGE INTENTIONALLY LEFT BLANK



October 19, 2016 7

PROJECT TEAM

Ming Yang, NEL

David Learmonth, NEL

Zak Latif, NEL

John Morgan, NEL

Colin Tyrie, Clean H2O Services

Hanadi Rifai, University of Houston

Debora Rodrigues, University of Houston

Emily Sappington, University of Houston

Jingjing Fan, University of Houston

John Vicic, Deepwater Innovation Consulting

Nicolau Danilovic, Digitrol

Paul O B ie , J.M. Canty

Colin Dalton, J.M. Canty

Thomas Friis-Eriksen, ProAnalysis

Hallvard Tangen, ProAnalysis

Mark Seator, Fjords Processing

Stewart Esslemont, Fjords Processing

Gary Foote, Fjords Processing

Chris Henderson, Fjords Processing

Erica Tait, Fjords Processing

Charles Dalton, Consultant

Nicholas Bourgeois, Clearview Subsea

Stewart Baskin, Clearview Subsea

Zetong Gu, Clearview Subsea

Pranab Jha, Clearview Subsea

Jianfeng Zhang, Clearview Subsea

Additional project team members and attendees to the industry workshops are shown in the attached

reports.



October 19, 2016 8

PROJECT SUPERVISION

Project Manager

William L. Fincham, National Energy Technology Laboratory

Technical Coordinator

James Pappas, Research Partnership to Secure Energy for America

Working Project Group (WPG)

Xiaolei Yin (Project Champion), ExxonMobil

Nikhil Joshi, Anadarko

Neal Prescott, Fluor Offshore Solutions

Alec Johnson, Petrobras

John Byeseda (former WPG member), Schlumberger

Jagadeesh Unnam, Schlumberger

Francisco Vera (former WPG member), Schlumberger

Diana Charles, Shell

Hamish McCracken, Shell

Steven Moseley, Statoil

Arne Henriksen, Statoil

Børre L. Knudsen, Statoil

Tatiana Issakova (former WPG member), Statoil

Mayela Rivero, Total

Khalid Mateen, Total

Herve De Naurois (former WPG member), Total



October 19, 2016 9

CONTENTS

Abstract ......................................................................................................................................................... 3

Acronyms and Definition ............................................................................................................................ 11

1 Introduction ........................................................................................................................................ 12

2 Technical Requirements for Subsea Pwd Sensors .............................................................................. 14

2.1 Methodology ............................................................................................................................... 14

2.2 Technical Requirements for Subsea PWD Sensors ..................................................................... 14

3 Gap Analysis and Ranking of exisiting technologies ........................................................................... 18

3.1 Technology Gap Analysis ............................................................................................................ 18

3.2 Ranking of Existing Technologies ................................................................................................ 20

4 Development of New Sensor Technology ........................................................................................... 22

4.1 Description of Technology .......................................................................................................... 22

4.2 Proof of Concept ......................................................................................................................... 22

4.3 Parametric Study on CLFM Measurement Principle ................................................................... 23

5 Subsea PWD Sensor Prototype Designs .............................................................................................. 26

5.1 Digitrol ......................................................................................................................................... 26

5.2 J.M. Canty .................................................................................................................................... 27

5.3 ProAnalysis .................................................................................................................................. 28

5.4 Clearview Subsea ........................................................................................................................ 29

6 Bench-Scale Tests ................................................................................................................................ 31

6.1 Test Requirements and Test Matrix ............................................................................................ 31

6.2 Test Program and Results ........................................................................................................... 33

6.2.1 Flow Loop and Tested Sensors ............................................................................................ 33

6.2.2 Reference Method .............................................................................................................. 38

6.2.3 Sensor Calibration ............................................................................................................... 40

6.2.4 Test Results ......................................................................................................................... 40

6.2.5 Discussions .......................................................................................................................... 45

6.2.6 Assessment of Sensor Performances .................................................................................. 52

7 Technology Readiness Level ............................................................................................................... 55

7.1 Justification ................................................................................................................................. 55

7.2 Note of Cautions ......................................................................................................................... 56

7.3 Other Important Parameters ...................................................................................................... 56

8 System Integration, Field Testing and Commercialization Plan .......................................................... 57

8.1 Improvements or Changes to Sensors ........................................................................................ 57



October 19, 2016 10

8.2 Technology Development Plan by Sensor Vendors .................................................................... 58

8.2.1 Digitrol ................................................................................................................................. 58

8.2.2 J.M. Canty ............................................................................................................................ 59

8.2.3 ProAnalysis .......................................................................................................................... 60

8.2.4 Clearview Subsea ................................................................................................................ 60

8.3 Multi-Sensor System Integration and Field Testing .................................................................... 61

8.4 Additional Considerations ........................................................................................................... 62

9 Conclusions ......................................................................................................................................... 63

9.1 Technology Readiness Level ....................................................................................................... 63

9.2 Technical Requirements on Subsea Produced Water Quality Sensors ....................................... 63

9.3 Technology Gap Analysis and Ranking of Existing Technologies ................................................ 64

9.4 Development of New Sensor Technology ................................................................................... 64

9.5 Subsea PWD Sensor Prototype Designs ...................................................................................... 64

9.6 Bench-Scale Testing of Subsea PWD Sensor Prototypes ............................................................ 64

9.7 System Integration, Field Testing and Commercialization Plan .................................................. 65

10 Recommendations .......................................................................................................................... 67

References .................................................................................................................................................. 68

Attachment 1. Research on EPA Method 1664 and Confocal Imaging

Attachment 2. Bench-Scale Test Requirements



October 19, 2016 11

ACRONYMS AND DEFINITION

API American Petroleum Institute

BSEE Bureau of Safety and Environmental Enforcement

CLFM Confocal Laser Fluorescence Microscopy

EPA Environmental Protection Agency

EPA E ui ale t HEM content obtained by using a correlation developed between HEM

measured by the IR analysis with Infracal 2 and HEM detected by the EPA

Method 1664B analytical procedure

EPA Method B The chemistry laboratory procedure used during bench-scale testing, which

analytical p o edu e follows the EPA Method 1664B with an exception of certain aspects of the

quality control requirements

FPSO Floating Production Storage and Offloading

FPS Floating Production System

GOM Gulf of Mexico

HEM N-Hexane Extraction Material

IR Analysis Infrared Analysis

JIP Joint Industry Project

LIF Laser Induced Fluorescence

MTBF Mean Time Between Failures

NETL National Energy Technology Laboratory

NIR Near Infrared Light

NPDES National Pollutant Discharge Elimination System

OiW Oil-in-Water

OIWM Oil-in-Water Monitor

ppmV Parts Per Million in Volume

PW Produced Water

PWD Produced Water Discharge

PWQM Produced Water Quality Monitor

RPSEA Research Partnership to Secure Energy for America

ROV Remotely Operated Vehicle

SCM Subsea Control Module

TRL Technology Readiness Level (API RP 17N)

UDW Ultra-Deepwater

WPG Working Project Group



October 19, 2016 12

1 INTRODUCTION

Subsea treatment and disposal of produced water is emerging as an important development option for

upcoming US Gulf of Mexico projects, in which large amounts of water are expected to be produced

from some of the fields due to water flooding for enhanced oil recovery. Separating water subsea and

then either discharging or re-injecting at the seabed brings many benefits both economically and

operationally. These benefits may include:

Lower total system cost: By moving the process equipment for treating and disposing most of

the produced water to subsea, the topsides weight and size can be significantly reduced, leading

to a lower cost of surface production systems (fixed platform, FPS or FPSO).. The flowline size

can also be reduced, further lowering the development cost.

Reduced environmental risk: Removing water from the production system reduces the potential

risk of corrosion and leaking in subsea flowlines and risers.

Improved flow assurance: Removing water from the production system also reduces the

potential risk of gas hydrate formation and blockages of flowlines. Also a much smaller amount

of hydrate inhibitor would be required compared to what would be needed if the produced

water is brought to the surface.

Several key technology gaps exist for such an option to materialize. One of them is related to the

availability of robust and reliable produced water quality measurement sensors that can accurately

determine whether the treated produced water meets the requirements for subsea discharge or re-

injection.

A number of online oil-in-water monitoring technologies have been developed and applied for surface

operations. These technologies have been known to provide operators with very useful information in

monitoring the separation and water treatment processes. However, to date few have been approved

for regulatory compliance monitoring, and certainly none in the U.S. These online monitoring

technologies have never before been tested for the purpose of subsea produced water discharge quality

monitoring applications.

The current project, funded by National Energy Technology Laboratory (NETL) and managed by Research

Partnership to Secure Energy for America (RPSEA), aimed to progress the subsea produced water

sensors to Technology Readiness Level 3 (API RP 17N), i.e., function/performance tested. The project

focused on developing subsea produced water discharge quality monitoring sensors. Findings of the

program are also applicable to subsea produced water re-injection operations where high water quality

specifications are required, such as anticipated for many of the new deepwater and ultra-deepwater

reservoirs.

The project was started in September 2014 and completed in September 2016. Figure 1 summarizes the

main project activities. There were two phases. The work in Phase 1 was to develop subsea produced

water sensor requirements by collecting industry and regulatory inputs for U.S. offshore applications,

analyze the technology gaps in existing sensors, perform proof-of-concept of a new technology, and

select sensors for further development in Phase 2. The work in Phase 2 was to design and construct



October 19, 2016 13

prototypes of the selected sensors, perform bench-scale testing of the sensors, and recommend a way

forward.

This report is the Final Project Report. It summaries the results and findings from the project. Each

chapter is related to a specific project task. More details on the specific project tasks can be found in the

references or the attachments.

Figure 1. Flow chart of project activities



October 19, 2016 14

2 TECHNICAL REQUIREMENTS FOR SUBSEA PWD SENSORS

This chapter summarizes the technical requirements developed during the project for subsea produced

water discharge sensors. Details are provided in Ref. 1.

2.1 Methodology

The technical requirements for subsea produced water discharge sensors were developed with the

following steps:

Assessment of the technology status on the use of online and inline oil-in-water monitors;

Collection of input from industry experts;

Discussions with regulatory agencies.

The assessment of the technology status was conducted with a report issued to the WPG members at

the beginning of the project. A one-day industry workshop was then held on December 16, 2014 in

Houston to collect industry input on the technical requirements for subsea produced water discharge

quality sensors. The workshop was participated by 27 experts representing operators, subsea system

suppliers, engineering companies and consultants, standards organizations, and research institutions.

Preliminary sensor technical requirements were then developed following the workshop.

Regulatory agencies (BSEE and EPA) were subsequently contacted to discuss the use of subsea produced

water quality monitoring sensors and the newly developed technical requirements, and to seek their

input. Both the BSEE and EPA did not have specific input to the sensor technical requirements at the

time due to the fact that using an online monitor for regulatory compliance monitoring is a relatively

new subject for them even for the surface applications. It is understood that EPA is developing a

protocol for accepting online monitors for Method Defined Parameters which would be applicable to Oil

and Grease. However the EPA did provide some literature on the subject, including a previous study on

utilizing different monitors to determine oil content, and also an example of using online monitor to

substitute laboratory procedure for a non-method defined parameter.

The technical requirements were finalized after receiving the feedback from BSEE and EPA.

2.2 Technical Requirements for Subsea PWD Sensors

Table 1 lists the technical requirements for subsea produced water discharge sensors that were defined

during Phase 1 (Doc. No. 12121.6301.03.01; See Attachment 1). The key requirements for the sensors

are presently considered to be as follows:

Measuring the oil and grease content in produced water for NPDES compliance (or alternative

compliance) reporting with readings that can be correlated within ±15% of those from the EPA

Method 1664B, or statistically equivalent to EPA 1664B. The sensors can be periodically

validated or re-calibrated;



October 19, 2016 15

Providing produced water quality information that would give warning and alarm signals with

the required confidence level to the subsea control system. Providing measurements once per

hour or faster in a quality suitable for making shutdown or diversion decisions on;

Meeting the above requirements for oil concentration over the range of 0 – 100 mg/L, covering

oils with API gravity of between 20 – min. 35/possibly 60 oAPI oil density, up to 250,000 mg/L

produced water salinity, with up to 100 mg/L of solids and up to 0.5% of gas (volumetric

fraction), and with concentrations of production chemicals in the produced water that are

typical of deepwater U.S. GOM developments;

Design condition 0 – 10,000 psig in pressure and 33 – 300oF in temperature;

Operating condition in 0 – 5000 psig in pressure and 38 – 200 oF in temperature;

Water depth up to 10,000 ft;

Design service life of 20 years and Mean Time Between Failure (MTBF) of at least 5 years;

Compliant with Subsea Instrument Interface Standard.

The following are not required of the sensors:

(1) Toxicity measurements, since it is anticipated that toxicity testing for subsea discharges will be

performed by surface laboratory tests with samples obtained at the subsea discharge location.

For surface discharge, NPDES General Permit requires toxicity test once per calendar year for

water discharge flowrate up to 4,599 bbls per day, and once per quarter for higher discharge

rate

(2) Free oil discharge monitoring that applies for surface operations by visible sheen observation.

Free oil monitoring requirements are to be determined pending further regulatory agency input;

(3) Flow measurement. NPDES General Permit requires the recording of once per month an

estimate of the flow. The estimate can be achieved by a number of means from other

instruments or sensors on the produced water system, such as flow meter or pressure sensors.

Therefore it is not necessary for the water quality sensor to measure the flow.



October 19, 2016 16

Table 1. Technical Requirements for Subsea Produced Water Discharge Sensors

Parameter Requirement

Oil concentration (mg/L) 0 – 100

Oil Density 20o – min 35 (consider 60) oAPI

Oil d oplet size μ 0 – 30

Solid concentration (mg/L) 0 – 100

“olid pa ti le size μ 0 – 200

Entrained gas in water (volume fraction) 0.5%

Gas bubble size (μ ) 20 - 100

Minimum Sea water temperature (oF) 33 (for Gulf of Mexico)

28 F / -2 C as next step (for Arctic)

Design temperature (oF) 33 – 300

Next step is 350

Design pressure (psig) 10,000 psi. Next step up is 15,000

Water depth (ft) Up to 10,000

Maximum flow velocity (ft/s) Up to 15 desired

For manufacturer to determine the limit for the

specific sensor

Operating temperature (oF) 38 – 200

Operating pressure (psig) 0 – 5000

Accuracy (%) Possibly 15%, or statistically equivalent to EPA

1664. Contingent upon decision by EPA.

Defined as difference with EPA 1664

measurements after calibration parameters are

set. Details to be determined.

Response time Hourly or faster – operator specific

Design service life 25 years

Calibration Required

Sensor Cleaning As required for proper functioning of the sensor

Mean Time Between Failures 5 years Minimum

Maintenance



October 19, 2016 17

Parameter Requirement

Maintenance provided through umbilical No limit

Maintenance provided by ROV Max once per quarter (4 times a year)

Repair by Retrieval to Surface Max once per 5 years

Max weight: Light intervention class - TBD

Shock and Vibration Per ISO 13628-6. Q1: 5g. Q2: 10g.

Production chemicals in Produced Water –

Concentrations

Hydrate inhibitor – Methanol 0 – 30% weight

Hydrate inhibitor – LDHI As typical for U.S. GOM deepwater projects

Wax inhibitor

Asphaltene inhibitor As typical for U.S. GOM deepwater projects

Asphaltene solvent - xylene As typical for U.S. GOM deepwater projects

Corrosion inhibitor As typical for U.S. GOM deepwater projects

Scale inhibitor As typical for U.S. GOM deepwater projects

Scale solvent As typical for U.S. GOM deepwater projects

Emulsion breaker As typical for U.S. GOM deepwater projects

Deformer As typical for U.S. GOM deepwater projects

Completion fluid As typical for U.S. GOM deepwater projects

Produced Water Properties

Salinity – total dissolved solids 0 to 250,000 ppm

Primary Cations Ca, Mg, Na, Ba, K, Fe

Primary Anions Cl, SO4, CO2, HCO3, F, B(OH)4, Br, CO3, I



October 19, 2016 18

3 GAP ANALYSIS AND RANKING OF EXISITING TECHNOLOGIES

This chapter summarizes the technology gap analysis conducted during Phase 1 of the project and the

ranking of existing technologies for further development in the project. Details are provided in Ref. 2.

3.1 Technology Gap Analysis

A workshop was conducted in which industry experts were given the opportunity to share their views on

the technology gaps in developing a subsea produced water discharge measurement sensor and how to

close these gaps. The workshop also allowed the attendees to provide their feedback on the proposed

technology gap analysis and technology ranking methodologies.

The gap analysis methodology was based on comparing the current capabilities of existing sensors to the

technical requirements and also on TRLs (as defined in the API RP17N).

Existing (with topsides or subsea installations) and new technologies that have the potential for being

developed for subsea applications are summarized in Table 2. Taking into considerations of the sensor

technical requirements, p oje t tea s k o ledge on sensor capabilities, the technologies listed in Table

3 were selected for the technology gap analysis and subsequent ranking.

Technology gaps identified from the workshop were in the following areas:

Regulations for subsea discharges;

Performance (e.g., low measurement accuracy);

Reliability (e.g., low availability, MTBF, and fouling);

Representative subsea sampling (for verification/measurement);

Testing (large uncertainties associated with reference methods, lack of purposefully built

facilities);

Standards (both for qualification testing and subsea instrument design);

Installations / system integration.

Workshop discussions also pointed out that actions would be required to close the technology gaps.

These may include:

The industry would need to share information and results as well as provide funding for sensor

development (as the market is small for the vendors);

Regulators i ol e e t would also be needed for better defining the discharge requirements

for subsea applications;

Vendors should have an adviser from a subsea system / engineering company to provide

expertise in marinization / system integration;



October 19, 2016 19

The parameter of oil and grease needs better definition;

A credible test program needs to be developed. And the industry needs to continue the

development of fouling mitigation technologies.

Table 2. Summary of the Potential Technologies

Technology Oil

Content

Solid

Content

Particle size &

distribution

Existing

Topside

applications

Existing Subsea

applications

Ultrasonic

Microscopy

LIF

UV fluorescence

Light scattering

Photo acoustic

NMR

Confocal LF & M

LIF and M

Note: M ea s i os op . LIF – Laser Induced Fluorescence; NMR – Nuclear Magnetic Resonance

Table 3. Technologies / Vendors Selection

Vendor

Technology Reasons for Selection

Advanced

Sensors

LIF LIF increasingly used for OIW monitoring

Inline probe available

Ultrasonic cleaning for fouling mitigation proved to work at low

pressure

Fastest growing company in the OIW monitor market

Keen to develop a subsea sensor

Already involved in other projects / JIPs

Digitrol Light

Scattering

Light scattering well established, popular for the ship bilge water oil

content monitoring

Unique fouling mitigation (self-cleaning) approach

Key reason for the selection is that a unit has been developed and

deployed for the Marlim subsea separation and PWRI application

J.M. Canty Microscopy Inline probe available


Double sided jetting for fouling mitigation

Worked closely with an operator on subsea sensor

Already involved in other JIPs



October 19, 2016 20

Vendor

Technology Reasons for Selection

Jorin Microscopy Best known microscopy supplier for PW applications

Willing to develop a subsea sensor

Jetting for fouling mitigation

Involved in other JIPs

Mirmorax Ultrasonic Only system that is not optical based

Staff is experienced with developing subsea technology


Involved in other subsea sensor development projects

ProAnalysis LIF LIF increasingly used for OIW monitoring

Inline probe available

Ultrasonic cleaning for fouling mitigation proved to work at low

pressure

Also keen in developing a subsea

Already involved in other projects / JIPs

Turner

Design

Conventional

UV

Best established conventional UV OIW supplier

Probably supplied the most OIW monitors for surface applications

3.2 Ranking of Existing Technologies

The ranking methodology was based upon an assessment on three elements:

Element A: How well does the technology meet the technical requirements for subsea produced

water discharge sensors?

Element B: How well is the technology placed in the API RP17N TRL table?

Element C: How prepared is the vendor to develop a subsea produced water discharge sensor?

The first two elements are essentially the gap analysis aspects. The last bullet is about capturing the

e do s a ilit / apa ilit , i te est, pla a d o it e t to de elop su sea ate ualit measurement devices. It was considered that not only the te h olog , ut also ea h e do s a ilit a d willingness to develop its technology would be important in the successful development of subsea PWD

sensors.

The ranking point was calculated by R = AX + BY + CZ, where X, Y, and Z are the respective weighting

factors for Elements A, B, and C. The weighting factors were assigned following feedback from the WPG

members.

The overall ranking points are shown in Table 4. More details can be found in the Attachment 2.



October 19, 2016 21

Table 4. Overall Ranking Points for the Sensors

Based on the gap analysis and ranking exercise conducted on the existing sensors as detailed above, the

top four ranked technologies for further development in the project were determined to be:

1. Digitrol (light scattering)

2. J.M. Canty (microscopic imaging)

3. Advanced Sensors (laser induced fluorescence)

4. ProAnalysis (laser induced fluorescence)

Digitrol s light scattering sensor, the top ranked sensor for Phase 2 development, is the only produced

water quality sensor with a subsea installation and some subsea operation track record. The total

ranking points are close between Digitrol and J.M. Canty, and between Advanced Sensors and

ProAnalysis.

Advanced

Sensors (LIF)

ProAnalysis

(LIF)

Canty

(Microscopy)

Jorin

(Microscopy)

Mirmorax

(Untrasound)

Digitrol (L

Scattering)

Turner Design

(Conv. UV)

Value A 55% 5.06 4.81 6.40 5.06 4.94 7.44 3.72

Value B 10% 7 7 7 7 7 10 4

Value C 35% 9.4 9.55 9.55 8.2 6.1 8.2 4.45

TOTAL 6.77 6.69 7.56 6.35 5.55 7.96 4.00

Parameters Weighing

Factor

Technology



October 19, 2016 22

4 DEVELOPMENT OF NEW SENSOR TECHNOLOGY

Confocal Laser Fluorescence Microscopy (CLFM) is a new technology concept for measuring oil content

in water. The measurement principle has the potential of not being affected, or little affected by

changes in produced water parameters during operation, such as oil droplet sizes, presence of solids, oil

coated solids, gas bubbles and others.

The development of the new sensor technology is summarized below. Details are provided in Ref. 2,

Appendix 7 for the proof of concept performed during Phase 1, and in Attachment 1 for the parametric

study on the measurement principle during Phase 2.

4.1 Description of Technology

The CLFM technology utilizes three principles for measuring oil droplets with its ability to optically

resolve droplets with sizes as small as 0.3 microns:

Laser Induced Fluorescence: some components of oil can emit fluorescent light when excited by

laser. This is utilized to distinguish the oil droplets from water and others, e.g. non-oil coated

solids;

Fluorescence Confocal Microscopy: confocal microscopy improves the resolution over

conventional (wide field) microscopy by filtering out most of the light contribution from out of

focal plane emission sources, and enables to create a high-resolution 3-dimensional image of

the view volume;

Deconvolution: utilized to further reduce the out-of-focal plane light contribution for improving

the i age s sig al to oise atio.

The resulting images can be analyzed to determine the type of objects identified (whether they are oil

droplets or oil coated solid particles), the sizes of the oil droplets and the total volume of oil taken up by

the droplets. Figure 2 shows an example of oil droplets imaged from a produced water sample with a

laboratory confocal microscope. The imaging operation requires the sample to be stationary; therefore a

valve was used in the flow setup to start and stop flow.

A schematic for a subsea PWD sensor with CLFM is shown in Figure 3. The sensor will be installed on a

side stream to the produced water flow line. The microscope and controller for the sensor will be

housed in a chamber under atmospheric pressure. The valves for the sensor will be submerged in

seawater.

4.2 Proof of Concept

The work on the proof of concept of CLFM included the following:

Defining the objective for the CLFM technology for subsea PWD applications;



October 19, 2016 23

Ide tif i g the eeds o su sea PWD ualit o ito i g a d e aluati g hethe CLFM s capabilities meet the needs;

Performing measurements of oil and grease by both the EPA Method 1664 B and CLFM, and

evaluating whether CLFM can be correlated with the EPA Method 1664 B;

Addressing the prevention and mitigation of fouling of the optical view window in the sensor;

Developing a conceptual design of subsea PWD sensor with CLFM to evaluate its feasibility for

subsea applications and to identify technology gaps.

A study was conducted to compare the measurements by CLFM to those by the EPA Method 1664B to

determine whether they were good correlation between the two. Table 5 compares the measurements

of oil concentration in several samples, both synthetic and field produced water, between the EPA

Method 1664 B and the CLFM. It can be seen that the CLFM measurements were generally in line with

the EPA Method 1664 B measurements.

Factors affecting the feasibility of CLFM for subsea use are summarized in Table 6. However these

factors can all be satisfactorily addressed as described in the same table. Tests on the jet cleaning

concept confirmed that it had the potential to be very effective in mitigating fouling resulted from oil

deposition. Additionally, a subsea concept design was developed which confirms the suitability of CLFM

for subsea applications.

The proof of concept study concluded that CLFM would be suited for being developed into a subsea

produced water discharge quality sensor. Its capabilities meet the subsea PW sensor needs, and CLFM

would have potential advantages in the accuracy of measuring oil content in produced water discharge

over others because of the high optical resolution 3-D imaging, which is utilized to accurately account oil

on solids, in clump groups and oil droplets behind other droplets.

4.3 Parametric Study on CLFM Measurement Principle

Additional research was conducted during Phase 2 to study the effect of parameters on the

measurement principle. The parameters included oil type, water salinity, solids, pH, and temperature.

The results confirmed the viability for using confocal to determine the concentration of oil in water in

the 25–50 mg/L range. Confocal results showed a wider range of variability in concentrations/recoveries

than EPA1664 depending on the specific conditions of the sample (type of oil, oil concentration,

environmental factors, and particles), but overall it was of sufficient accuracy to determine the

concentrations of oil. It is important to point out that more accurate determination of the oil

concentration using confocal microscopy will require more measurements (each with a small volume, in

order of microliter) to be analyzed than with the EPA method (1 L sample).



October 19, 2016 24

Figure 2. Example 3D image stacks generated with confocal microscope on produced water sample.

Figure 3. Schematic of subsea CLFM sensor for measuring oil content of produced water

ROV1

ROV2



October 19, 2016 25

Table 5. Comparison between Oil and Grease Results from the CLFM and the EPA Method 1664 B

Sample

Prepared

Conc.

(mg/L)

EPA Method 1664 Confocal Analysis

Measured

Conc.

(mg/L)

Standard

Dev.

Diff.

between

meas. &

prep.

(%)

Measured

Conc.

(mg/L)

Standard

Dev.

Diff.

between

meas. &

prep.

(%)

Synthetic 28.67 37.70 13.7 31% 25.47 9.85 -11%

Synthetic 47.79 39.40 6.01 -18% 42.95 10.57 -10%

Field PW

Clea

N/A 6.56 0.78 N/A 4.88 1.57 N/A

Note: All results were the average of measurements performed on triplicate samples.

Table 6. Factors Affecting for the CLFM’s Feasibility for Subsea Use

Factors Status

Functional Performances in Subsea

• Imaging operation

• Image processing

Conceptual design and initial laboratory testing indicate feasibility

to provide 1 reading every 2 minutes, and the stability of the

statistics after several readings.

Interfaces with Subsea System Interfaces with subsea system (hardware, control system and

chemical system) identified and considered feasible.

Constructability, Installability and

Retrievability

• Availability of Components

• Dimensions and Weight

• Shock and Vibration

• All key components except for the microscope objective lens

were identified to be available as off-shelf products. Microscope

objective vendors confirmed capability for custom design to

extend the cover glass thickness correction to the

10,000/15,000 psi requirement.

• Preliminary dimension and weight developed and are

considered well within offshore equipment limits

• Shock and vibration are conceptually considered as manageable

and will be further developed during the next phase

Maintenance Maintenance requirements are typical of normal subsea system

operations.

Reliability Reliability of each component considered. Conceptual design

indicates ability to meet the 5-year MTBF requirement.



October 19, 2016 26

5 SUBSEA PWD SENSOR PROTOTYPE DESIGNS

At the beginning of Phase 2, designs of the subsea PWD sensor prototypes were conducted for the

technologies that were selected at the end of Phase 1 (with the exception for ProAnalysis as discussed

below). The designs were carried out according to a design basis that details the technical requirements

for the sensors as developed in the Phase 1. Two designs were developed for each technology using the

same measurement principle, one for the subsea environment and one for bench-scale testing

environment.

This chapter summarizes the designs. Details, including the design basis and the individual design

reports, are provided in Ref. 3.

5.1 Digitrol

Digit ol s desig (Figure 4) is based on the following measurement principles:

Oil Measurement: NIR light scattering for low ranges (typically 0 – 200 ppmV) and NIR

Absorbance for high ranges (typically 0 – 1000 ppmV).

Fouling mitigation strategy: fluid hydrodynamics forces (vortices and turbulences using a

Venturi configuration and an obstacle) generated around the measurement area prevents

fouling to occur in the first place.

The logarithmic relationship between both NIR beams (straight and scattered) measured by the sensors

is proportional to the oil concentration in the sample. The success of this method, as any optical

methods, depends on the optical windows staying clean. This is achieved by the hydrodynamics created

with its unique internal design as shown in Figure 4. The instrument also has the capability to

compensate for small dirtiness using measuring beams referring to direct light beam.

The subsea sensor is comprised of the following components:

Subsea Monitor in Stainless Steel SUPER DUPLEX Standard UNS 32.750 ASTM 182-F53 and ASTM

A479 S32750 used in all wetted part with measurement optoelectronics devices. The Subsea

Monitor should be recovered by ROV.

Connection cable between Subsea Monitor and the Subsea Control Station (part of customer

subsea installations): Teledyne ODI Nautilus jumper, ROV or manual-mate cable ends, and wet-

mating connectors.

Surface Controller Module: Communicates with Subsea Monitor through a RS485 network

provided by customer (PLC or similar in a Surface Control Room, connected to a Subsea Control

Station through the umbilical). The Subsea Control Station must have a Nautilus bulkhead

receptacle (sockets) to mate Nautilus jumper cable end plug (pins). Connector parking position

may be expected but not reinforced at this time.

A bench-scale version was also designed. The design is similar to that for the subsea design but differs in

the following two respects:



October 19, 2016 27

1) The electrical connector is a dry-mate, topsides connection cable;

2) The design pressure and flowrates are for bench-scale testing only.

Figure 4. Digitrol Sensor

5.2 J.M. Canty

Ca t s desig fo su sea dis ha ge appli atio s uses thei topsides version of visually based Inflow

model. For the bench-scale prototype a reduced pressure of 150 psig was specified. Canty used a

standard topside system for testing since the imaging and illumination components would be the same

as those used for the subsea design. The concept for the subsea design was to add to the standard

topside system with a new environmental housing to withstand the external pressure as well as a

separate electronic enclosure to house all control electronics items.

As in all Canty systems currently the source of illumination is a LED. With regard to subsea applications

the primary considerations for illumination have to do with heat generation and dissipation, and life

expectancy. Normal filament type bulbs, regardless of type or usage schemes, do not have good life

expectancy. Plasma type sources, although very bright, have noisy output characteristics not well

matched to imaging requirements, and are difficult to cool due to their tremendous heat generation.

The LED is the best combination of illumination intensity, life span, cost and thermal profile for this

application.

The visual components of the Canty InFlow systems for OIW are constantly improving. At present the

lens / camera system provides for a high resolution image which can capture particles/droplets down to

1 micron which is needed for reliable analysis in the 1 – 2500 ppm range of OIW. The design concept for

this project was to sample continuously from the main flow line, pass the sample through the Inflow



October 19, 2016 28

device and back to the flow line. This design concept offers advantages over inline systems for subsea

applications.

Canty fuses glass to metal to create a rugged, high pressure lens that can handle 10,000 psig while

providing a clear, smooth contact surface to the process fluid. A clear image can be taken and a highly

polished internal surface optimizes cleanliness.

Although the instrument can be designed and installed directly into the flow line, a better option is to

install in a bypass / return loop so that the instrument can be removed for maintenance without

disturbing the flow line. Figure 5 shows the bench scale testing model with a sampling loop which

flanges into line, diverts fluid into the extraction tube, through the instrument, and back around and

into the flow line.

Fouling in crude oil applications is always a concern. Canty has designed and patented a cleaning system

specifically for this type of application that delivers the cleaning fluid with a high velocity onto the

optical window.

Figure 5. J.M. Canty Inflow Short Loop Sampler Design for Subsea Applications

5.3 ProAnalysis

ProAnalysis OIWM uses laser induced fluorescence as the measurement principle. Since the company

was under certain restrictions regarding the release of some information about their subsea designs due

to an ongoing participation in another subsea OiW sensor development project, specific designs for the



October 19, 2016 29

current project were not provided. ProAnalysis provided a standard low pressure topsides unit for

bench-scale testing, Model No. SWC-1-A-00-00-A-1-N-1-A-0-A.

The unit is consisted of:

Control Unit in Safe Area

OIWM Probe with Retraction Tool (shown in Figure 6).

The probe includes an ultrasonic cleaning device, which is periodically activated to remove oil, grease,

scale, and other fouling deposits on the probe.

Figure 6. ProAnalysis OIWM Probe

5.4 Clearview Subsea

Clearview Subsea developed two Confocal Laser Fluorescence Microscopy (CLFM) prototype designs for

a subsea produced water discharge quality sensor. One prototype design was for the subsea conditions,

and the other was for laboratory conditions for the bench-scale testing in the project. The designs were

developed according to the service conditions and sensor requirements.

The emission of fluorescence from oil under excitation by laser is one of the two physical principles of

the CLFM method. Confocal microscopy is the other physical principle of the CLFM method. A point-

scanning microscope with an objective mover was used in the prototype design.

The sensor includes the following main components, as shown in Figure 7:



October 19, 2016 30

Spool Piece for Main Discharge Pipe

Sample Piping

ROV Access Module (subsea design only)

Cleaning Module

Microscope Module

Sensor Control Module

Dry Nitrogen-Filled Pressure Chamber (subsea design) or protection cover, which houses the

microscope, measurement section and sensor control modules

The optical windows are periodically cleaned with liquid jets. The cleaning liquid is methanol in the

subsea prototype design and water in the laboratory bench-scale prototype design. Solvent soaking can

also be performed on the subsea sensor with a ROV if required.

The sensor will be calibrated prior to installation. Once installed and in operation the sensor can be

validated and calibrated subsea. For validation, produced water samples will be taken by a ROV from the

subsea sampling point and measured in a surface laboratory with the EPA Method 1664 B. The se so s readings at the time of the sampling will be compared with the EPA Method 1664 B measurements.

Calibration subsea may be done either using a similar procedure to that of the validation process, or by

ROV connecting with the calibration/solvent soak connection on the sensor and circulating a calibration

fluid with a known oil-in-water concentration.

Subsea Configuration Microscope and Measurement Section

Figure 7. CLFM Sensor from Clearview Subsea



October 19, 2016 31

6 BENCH-SCALE TESTS

6.1 Test Requirements and Test Matrix

The test requirements for bench-scale testing were developed for evaluating the sensors functions and

performances against operating parameters that are important to subsea produced water discharges. In

developing the test requirements, international standards such as ISO 15839 that had been specifically

developed for testing and evaluating online water quality measurement sensors were considered. Also,

experiences and lessons learned from previously conducted relevant JIP testing were incorporated.

This section summarizes the test requirements. Details are provided in Attachment 2.

The tests were required to include the effects of the following parameters: oil droplet size, solids, gas

bubbles, chemicals, temperature, flow velocity, API gravity and salinity. The oil concentration measured

by the sensors is then compared with the Oil and Grease measured using the EPA Method 1664 B.

Memory effect tests and fouling mitigation tests were also required.

Table 7 summarizes the tests conducted. The baseline parameters for the bench scale tests were:

• Salinity: 35,000 mg/l TDS, typical of seawater

• Flow velocity in the test section (3inch spool): 3 m/s

• Temperature: ambient (11 to 13 oC)

• Oil droplet size: 15 µm

• API of crude oil: 30o

Tests using real produced water were initially considered but ultimately not included in the bench scale

test requirements due to the logistics involved and the anticipation of potential changes in its

characteristics resulted from, for example, scale precipitation and settling of oil droplets. Furthermore,

it was thought that using synthetic produced water could meet the objectives of the bench scale testing

and would not require special handling.

Tests of se so s fouli g itigatio e ha is s u de an elevated pressure were also considered but

not included. Tests of se so s fouli g itigatio e ha is s u de a ele ated p essu e were not

thought to be a priority in the current stage of the technology development. Three of the sensors tested

use high velocity flow for cleaning or preventing fouling. The fouling mitigation mechanisms were not

thought to be sensitive to the pressure. The other sensor uses ultrasonic for cleaning, which is currently

only applicable in low (near atmospheric) to moderate (a few hundred psi) pressure environments, and

therefore it was recognized that further technology development would be required if it were to be

used for subsea applications.



October 19, 2016 32

Table 7. Bench-Scale Test Matrix

Test Types API

Oil Solids Gas

Size

Chem.

(mg/l)

Temp.

(oC)

Manual

Induced

Fouling

Flow

rate

(m/s)

Salinity

( ’s mg/l)

Conc.

(mg/l)

Size (µm) Conc.

(mg/l)

Size

(µm)

Oil only

[Note 1]

15,

20,

30,

40

10, 15, 20,

25, 30, 50,

100, 200

~15

Salinity 30

10, 20, 30,

50, 100,

200

~15

35,

100,

250

Oil & Solids 30

10, 20, 30,

50, 100,

200

~15 10, 50 5, 10

Effect of Gas

bubbles 30 10, 30 ~15 0, 10 5 1 , 2

Effect of

Chemical

(Corrosion

Inhibitor)

[Note 2]

30 10, 30, 50 ~15 40

Effect of Scale

Inhibitor [Note

2]

30 10, 30, 50 ~15 40

Effect of

Hydrate

Inhibitor [Note

2]

30 10, 30, 50 ~15 1%

Effect of

DeOiler [Note

2]

30 10, 30, 50 ~15 20

Effect of

Temperature 30 30 ~15

0.5, 3,

5, 25,

65, 90

Fouling Test 1

30 30 ~15 Oil soak

Fouling Test 2

30 30 ~15

Grease

applied

Memory Test 1

[Note 3]

30 30, 500, 30

~15, 30,

15

Memory Test 2

[Note 3] 30

30, 2000,

30

~15, 50,

15

Memory Test 3

[Note 3] 30

30, 5000,

30

~15, 50,

15

Effect of Flow

Velocity

[Note 4]

30 30 ~15 0, 10 5 1.5,

3, 4.5

Effect of

Droplet Size

[Note 5]

30 30 5,10,

15,20

Notes:

1. The runs were repeated at the end of the test program for the 30 oAPI oil.



October 19, 2016 33

2. The chemicals and specific concentrations used in the test program were recommended by a major

chemical supplier for offshore oilfields.

3. When a very high oil concentration is required, there is a limitation of the test rig in generating a very

small oil droplet size. For the memory tests, 500 mg/l, 2000 mg/l and 5000 mg/l oil-in-water were

needed. The oil droplet sizes were respectively set at 30, 50 and 50 µm.

4. For the velocity effect tests, additional runs were conducted with solids added.

5. The runs were conducted at the beginning of the test program and repeated toward the end of the test

program.

6.2 Test Program and Results

Bench-scale testing of these oil-in-water monitors (OIWMs) was performed from June 20 to September

15, 2016 at Fjords Processing Ltd., Orkney, Scotland, UK. The tests conducted are summarized in Table 7.

The o je ti es of the tests e e to e aluate the se so s pe fo a es o easu i g oil o te t he compared with reference measurements (Oil & Grease by EPA Method 1664B), and to assess how the

measurement performances were affected by the test parameters.

The following subsea produced water sensor prototypes were tested, from the bottom to the top of the

vertical test section of the flow loop:

• Clea ie “u sea s CLFM se so

• J.M. Ca t s I Flo se so ith a short loop sampler

• P oA al sis A gus LIF p o e

• Digit ol s TOG light s atte i g se so

The test program, results and analysis are summarized below. Details are provided in the Subsea

Produced Water Discharge Sensor Lab Test Results and Recommendations Final Report (Ref. 4), which

also contains the reports or notes that the four vendors have provided on their participation in the test

program.

6.2.1 Flow Loop and Tested Sensors

A o e-pass seawater flow loop at Fjords Processing was used for the tests. A PFD of the flow loop is

shown in Figure 8. Fjords pumps filtered seawater (filtration capable of removing 98% of raw seawater

media at 2 µm) from a balance tank to the OIWM test section. The flowrate through the test section was

measured and controlled via a flow meter and a control valve downstream the OIWMs. After the feed

pump and before the OIWMs there were various take-off and return lines for the injection and variation

of the test fluids, such as: oil, solids, gas, and chemical injection, and for cooling the water in low

temperature runs. Once the flow passed through the test section and a flow control valve the fluids

passed to the drainage system. The used test fluids eventually arrived at the Flotta Oil Terminal s water

treatment system.



October 19, 2016 34

The four OIWMs were installed serially in a vertical test section, as shown in Figure 9. Each OIWM was

separated by at least 10 pipe diameters to minimize any installation effect of upstream OIWMs. The pipe

leading to each of the OIWM as NB, e ept fo the Digitrol OIWM where the spool was reduced

do to ½ NB to provide a higher velocity that was initially considered to be required by Digitrol.

Pictures of the tested sensors are shown in Figure 10 to Figure 13.



October 19, 2016 35

Figure 8. Process Flow Diagram of the Bench-Scale Testing Flow Loop



October 19, 2016 36

Figure 9. Test Section of Flow Loop



October 19, 2016 37

Figure 10. Clearview Sensor. Left: Sampling spool piece of test section of on flow loop. Right: Pipework

and Analyzer

Figure 11. J.M. Canty Sensor. Upper Left: Model showing the sensor and sample piping. Upper Right:

Installed on test section of flow loop. Lower: Pump of cleaning system.



October 19, 2016 38

Figure 12. ProAnalysis Sensor. Left: Probe. Right: Cabinet and computer.

Figure 13. Digitrol Sensor. Left: Sensor installed on test section of flow loop. Right: Signal converter

and recorder.

6.2.2 Reference Method

The p o edu e fo o du ti g EPA Method B easu e e ts as de eloped at Fjo ds P o essi g s onsite chemistry laboratory. A rigorous qualification program was conducted that demonstrated the

la o ato s ability in performing the procedure competently. The laboratory also performed ongoing

precision and recovery tests to confirm the procedures performed met the requirements of EPA Method

1664B. Minor changes were made to the quality control program (frequency of QC samples measured)

and other procedure steps, taking into consideration of the much smaller number of samples to be

analyzed per day versus typical batch sizes for which the EPA Method 1664B QC requirements are

intended for. Consequently, the value obtained from this modified laboratory procedure is referred to

as Hexane Extracted Materials (HEM) with EPA Method 1664B analytical procedure. Figure 14 shows the



October 19, 2016 39

percentage recoveries of the IPRs and OPRs that were carried out over the bench scale test period. Both

the IPRs and OPRs were within the acceptable limits defined in the EPA Method 1664B, i.e. 83 to 101%

for the IPRs and 78-114% for the OPRs.

Figure 14. IPR and OPR for EPA Method 1664B Analytical Procedure

The EPA Method 1664B analytical procedure is time consuming. It typically took about 3 hours to get a

sample analyzed. For a normal test run, which lasted for 2-3 hours, only one sample can be realistically

completed. Consequently, an infrared (IR) analysis method using the Infracal 2 model ATR-SP, which

could be completed within 15 minutes, was used to correlate the HEM measured by EPA Method 1664B

analytical procedure. The value correlated from the IR analysis was referred to as considered the EPA

Method B e ui ale t o EPA E ui ale t in this report. An example correlation between the two

methods (for the 30 oAPI oil) is shown in Figure 15.

The InfraCal 2 principally operates in a similar fashion to the EPA Method 1664B analytical procedure,

whereby oil is extracted by n-hexane, and then quantified following the evaporation of the n-hexane

solvent. The main difference is that in the IR method, a small volume of the sample extract is left onto

an IR platform, after the evaporation of the solvent, IR light is directed and its absorbance is measured,

which is related to the oil concentration. Whilst in the EPA Method 1664B, the entire sample extract is

placed into a volumetric flask (pre-weighed), following the evaporation of the solvent, it is weighed

again, the difference in weight is related to the oil-in-water or HEM.

For each test run, it was decided that one sample would be analyzed with the EPA Method 1664B

procedures and at least three samples were analyzed with the IR method. Additional samples were



October 19, 2016 40

taken for analysis using the EPA Method 1664B procedure in case the EPA Equivalent result of the first

sample was significantly different from that by EPA Method 1664B analytical procedure, however these

analyses were not needed for the test runs.

Figure 15. Example of correlation between HEM Measured by IR and EPA Method 1664

6.2.3 Sensor Calibration

Oil samples were provided to sensor vendors (except for Digitrol) during the prototype construction

period so that in-house tests and calibrations can be conducted before the sensor was shipped to Fjords

Processing for the bench scale testing. Digitrol elected to perform the calibration onsite.

The sensor calibration was finalized during the sensor commissioning period of June 6 to 17, 2016. Flow

loop test runs with 20 and 30 API oils at various concentrations were made. A sample was taken in each

run, and an IR analysis was performed to obtain an EPA Equivalent value. The EPA equivalent values

were provided to the commissioning personnel from each of the sensor vendors.

6.2.4 Test Results

The sensors were generally able to perform measurements throughout the test program. Canty and

P oA al sis se so s e e a le to fu tio i all tests ithout i te e tio . Digit ol s se so had so e issues initially with optical window fouling that occurred during commissioning, but was able to self-

lea afte a fe da s of u s. Clea ie s se so eeded t o i te e tio s: Afte the temperature



October 19, 2016 41

effect tests, there was a small leak from the optical window which was repaired. Also the objective

location was adjusted to compensate for a permanent displacement of the measurement section

following the temperature effect tests; (2) One week before the end of the test program, t it was

noticed that the signal strength stayed very low. A quick check indicated that the laser generator

seemed to have drifted lower in output, and the laser intensity setting was increased.

Cha ges i the se so s easu e e ts ith the pa a ete a iatio s a d the e te t of the pa a ete s impact are summarized in Table 8. The summary is based on the real-time measurements of Canty,

Digitrol and ProAnalysis sensors, and post-processed results from Clearview. Clea ie s eal-time

measurements had occasionally (typically a few times per day) very large errors due to imaging

processing algorithm being too simplistic. These errors made the original online real-time results for

most runs not usable. The post-processed results more realistically reflect the impact of the test

parameters on the measurements from the CLFM measurement principle and the hardware

configuration. The post-p o essed esults a e sho i the epo t o Clea ie se so p otot pe s construction and testing, as one of the appendices of the Subsea Produced Water Discharge Sensor Lab

Test Results and Recommendations Final Report, Doc. No. 12121.6301.03.Final3 (Attachment 5).

The test results show that:

• The accuracy of each sensor (deviation from the EPA Equivalents) is affected by the test

parameters.

• The sensor measurements were not able to stay within 15% of the EPA Equivalents over the full

range of a test parameter.

• The sensor measurements were able to stay within 50% of the EPA Equivalents for some

parameters.

• Each test parameter affected the sensors differently. Each sensor has parameters that affect its

measurements moderately, and also parameters that affect significantly.

• Each sensor has two or more parameters for which the deviations from the EPA Equivalents

were more than 100% higher, or more than 75% lower.

• In the memory tests, the sensors were all able to recover after experiencing a high oil

concentration event indicating that memory effect is minimal or small.

• Digit ol s lea i g e ha is o ked ell fo oth fouli g tests. The othe se so s lea i g mechanisms worked well for the moderate fouling case of oil soaking, but looked to be unable

to clean the severe fouling case of manually applied grease. More tests may be needed in the

future to fully evaluate the apa ilities of ea h se so s fouli g itigatio e ha is .

In terms of individual parameters,

• The sensor measurements were most stable with oil concentration changes.

• The sensor measurements were also stable with flow velocity changes, except for the Digitrol,

which had extremely high readings at high velocity.



October 19, 2016 42

• Chemicals and salinity had more impact on the measurements than oil concentrations and

velocity.

• Oil droplet sizes, solid particles, gas bubbles, solid particles, temperature and oil density each

severely affected thee of the four sensors, where measurements at some parameter values

were either over 100% higher or over 75% lower than the EPA Equivalents. The list of the three

sensors vary depending on the parameter.

In terms of ea h se so s pe fo a e,

• The Canty sensor is least affected by oil concentration and fluid velocity. It is most affected by

oil droplet size, oil density, solid particles, gas bubbles, temperature, and hydrate inhibitor.

• The Clearview sensor is least affected by oil concentration and fluid velocity. It is most affected

by gas bubbles, temperature, oil density, and salinity. The effect of chemicals is not known due

to a need to repair the sensor during these tests.

• The Digitrol sensor is least affected by oil concentration and oil density. It is most affected by oil

droplet size, fluid velocity, solids, gas bubbles and temperature.

• The ProAnalysis sensor is least affected by oil concentration, fluid velocity, corrosion inhibitor

and salinity. It is most affected by oil droplet size and oil density.



October 19, 2016 43

Table 8. Impact of Parameter Variations on Sensor Measurements

Generalization were made in some cases for this high level summary of the parameter impact. Not all

run cases are represented by the trends shown. For detailed analysis of the results, refer to Subsea

Produced Water Discharge Sensor Lab Test Results and Recommendations Final Report, Doc. No.

12121.6301.03.Final3 (Attachment 5).

Parameter Variation

(Note 1)

Canty

(Note 2)

Clearview

(Note 3)

Digitrol

(Note 2)

ProAnalysis

(Note 2)

Oil Concentration ↗↘ TBD

(Note 3)

↗↘

(Note 10)

↘

(Note 11)

Oil Droplet Size

↗ ↗ ↘ ↘

Fluid Velocity

↗ Varying Varying ↘

Solids

↗↘ ↘ ↗ ↘

Gas

↗ ↘ ↗ ↘

Temperature (Full Range

Tested: 0.5 to 90oC)

↗

(Note 4)

↗↘

(Note 5)

Varying ↗↘

(Note 6)

Temperature

(Ambient to 90oC)

↗ ↗↘ ↘ ↘

Chemical – Corrosion

Inhibitor (Note 7)

↗ Not determined No clear trend

(Note 8)

Chemical – DeOiler

(Note 7)


(Note 8)

↘

Chemical – Hydrate

Inhibitor (Note 7)

↘ Not determined ↗

(Note 8)

↘

Chemical – Scale Inhibitor

(Note 7)


(Note 8)

API Gravity of Oil

↘ ↗ ↗

Water Salinity

↗ ↗ ↘ ↘

Memory Effect (Note 9)

Acceptable Acceptable Acceptable Acceptable

Fouling (Oil soaking and

applied grease)

Cleaned oil

soaking

Cleaned oil

soaking

Cleaned both Cleaned oil

soaking

(Note 10)

Notes:

1. The s ols illust ate the t e d of ha ge i the atio of se so s easu e e ts a e age alues to EPA Equivalent when the parameter increases. : Le el; ↗: I ease; ↘: De ease.

2. Based on online measurement values recorded during the tests

3. Based on post-processed values from images recorded during the tests. The post-processed data are

provided in the Clearview report, which is an appendix to the bench-scale testing report (Attachment 5).



October 19, 2016 44

Clea ie s o li e easu e e t alues ha e e essi e a iatio s due to li itatio s i i agi g p o essi g algorithm, which make the data not usable for evaluation of trends. The baseline runs are used for

calibration in the post-processing, so performance on oil concentration variation cannot be assessed.

Tests for repeat of baseline are to be analyzed pending access to the recorded images.

4. No readings at 0.5oC and 3oC.

5. No readings at 0.5oC and 5oC.

6. Values lower than baseline when the temperature is either higher or lower than baseline, but levelled

when the baseline is not included.

7. Range of parameter is from no chemical to with chemical. Clea ie s se so as offli e du i g ost of the tests with chemicals due to a need to repair the sensor.

8. Acceptable if high oil concentration is detected, and return to normal in 30 minutes after the high

concentration event has passed.

9. No change during the oil soak tests.

10. Based on repeat run results. The initial baselines may not be as reliable.

11. Large variation in initial baseline runs, but within -40% to 20% in repeat runs.

12. Variation within 50% in initial baseline runs, but -56% to -86% in repeat runs.

Color Coding:

Note that the color coding provided in the table is only for overall view of the test results. They do not cover all the

test results. For complete discussions on the results, see the bench-scale test report (Attachment 5).

The maximum difference between sensor measurement and EPA Equivalent is

15% or lower.

Memory tests: acceptable

Fouling tests: able to clean both oil soak and applied grease


over 15% and up to 50%

Fouling tests: able to clean one of oil soak and applied grease


over 50% and up to 100%. If negative, as low as -75% (which means the EPA

Equivalent to measurement ratio is 4).


over 100%. If negative, less than -75%.



October 19, 2016 45

6.2.5 Discussions

The test results showed that all sensors were affected by many test parameters. Some of the observed

effects can be straightforwardly related to the measurement principles of the sensors, while other

effects are not well understood at present.

Uncertainties in Test Conditions and Reference Methods

Uncertainties in test conditions, unrelated to the sensors, can also contribute to our assessment on

sensor s measurement performance, or accuracy defined as the de iatio of the se so s eadings from

the EPA Equivalent values. The uncertainties may include variations of the following during a run: oil

concentration, average droplet sizes and distribution, base water quality especially solids content,

content and size distribution of solids, and gas bubble size distributions. However, the test loop

operation has been carefully controlled to minimize the variations and uncertainties. Operation data

suggest that the variations are small, and in any case it was thought that these variations were much

lower than the changes in sensor readings when test parameter changes.

They are also uncertainties in the reference methods, including the measurements from the EPA

Method 1664B analytical procedure and the IR analysis method, as well as the conversion of IR analysis

results to the EPA Equivalents. Recovery factors with solvent extraction methods vary noticeably even

with well controlled procedures. For example, the Ongoing Precision and Recovery requirement by the

EPA Method 1664B is 78 – 114% for HEM. However, it is thought that the uncertainties associated with

reference methods did not have a significant bearing on the assessment of se so s performance for

following two reasons:

• The laboratory achieved good and consistent recoveries in the measurements for initial

precision and recovery, and maintained compliance ongoing precision and recovery (OPR)

throughout the test program. The OPR range was 81 – 102%.

• Other than a few outliers, the three EPA Equivalent values obtained in each run were close

to each other, and their averages were close to the HEM from the EPA Method 1664B

analytical procedure. The difference in most cases was much smaller than the difference

seen between the sensor s readings and the EPA Equivalents across test runs.

Therefore, uncertainties in test conditions and reference methods are generally not considered to be a

p i a fa to affe ti g the se so s a u a in the tests. Fjords Processing and sensor vendors have

identified some abnormal runs, for which the evaluation of the sensor accuracy took these uncertainties

into account. These include the runs with the hydrate inhibitor, for which Fjords Processing concluded

that HEM could not be accurately quantified in the presence of the hydrate inhibitor by the EPA Method

1664B analytical procedure.

Other possible outliers are related to the runs with high salinity. It is not absolutely certain at present

whether all the salt was fully dissolved before tests were conducted. If a substantial amount of

microscopic salt particles were present in the water, some sensors would be affected.



October 19, 2016 46

Ranges of Test Parameters



the sensors. It is beneficial to test the sensors for understanding their robustness and limits, but the

sensors may not have to perform at as high accuracy in these situations as more common parameter

levels, since the extreme conditions can be handled by alternative methods such as changing the

installation location, adding features to the subsea produced water system for mitigating the conditions,

and using risk-based approaches for utilizing the measurements.

For any single subsea development, the full ranges of the parameters may not necessarily occur. The

relative importance of the parameters for good performance of the sensors may also be different

between developments. When the results from the current project are used in the evaluation of

applicable produced water quality sensor technologies, it is important that the use is within the context

of development-specific requirements and key parameters.

Below are some examples of the extreme values included in the tests:

The content of solids was tested up to 200 mg/L. The high content is not expected to be a

regular event. It may occur in an upset condition or post operations on a well, which are either

detectable by other information on the produced water system or can be anticipated.

The gas content used is 0.5% volume fraction of water, which is much higher than what has

been experienced in surface produced water discharge. The value was selected to evaluate the

situation of gas coming out of solution due to pressure drop in hydrocyclones, which are

frequently considered for subsea use due to their compactness. To mitigate the effect of high

gas content on sensor accuracy, it may be possible to install the produced water quality sensor

at a location on the produced water system where the gas content is much lower.

While a real field application will unlikely encounter the kind of dramatic changes as our bench scale test

program was deliberately designed for, significant changes in produced water characteristics do occur

due to:

Process upsets

Process shutdown and re-start

Comingle of production fluids as new tie-ins / marginal oilfields are brought into production

Increase sand production due to an increase level of water and gas production

Very tight oil-in-water emulsion (small droplet size) due to the presence of certain types of

production chemicals

Thus the sensors will need to cope with variations in produced water characteristics (on top of being

able to cope with temperature and pressure both internal and external) subsea.



October 19, 2016 47

Effect of Oil Concentrations

The sensors were calibrated during commissioning by flow loop runs with 30 API oil at various

concentrations. A smaller number of commissioning runs were also made with 20 API oil. The runs for

evaluating oil concentration effect were performed with 30 API oil at a range of concentrations. The

sensors had the best accuracy in this set of runs. This confirms that the sensors can perform well under

conditions similar to those when the instrument is calibrated.

Effect of Droplet Size

The Canty sensor tends to under-estimate when the droplets are smaller. The video images were less crisp

with smaller droplet size tests compared to larger droplet size tests, which would lead to a lower detection

rate based on the same threshold settings. It seems that with a smaller oil droplet size, the sensor was

missing out a significant amount of oil droplets with the current pixel scale / resolution, and threshold

setting. As oil droplet size increased, the measurement became less affected since the fraction of

detectable droplets increased.

The Clea ie se so s eadi gs (with the re-processed data) increased with droplet size. This is due to

the effect of buoyancy. The prototype design was based on almost horizontal measurement section for

the oil droplets in the view volume to be either stationary or moving upward during imaging. However,

the measurement section in the prototype was at too high an angle from horizontal (7 – 10o).

Consequently, while imaging, some oil droplets which are originally outside the imaged volume may move

into view and get counted in the total oil volume. Since the velocity of the buoyancy driven motion is

proportional to the square of the droplet diameter, the over-count gets more severe with larger droplets,

leading to the overall trend of sensor reading increasing with droplet size.

In the range of oil concentrations tested, the Digitrol sensor measures the oil content by correlating the

strength of the scattered NIR light. With an increased oil droplet size, there was a smaller number of oil

droplets. With that, a smaller amount of scattered light was expected and hence the reduced readings

from the sensor.

The ProAnalysis sensor measures the oil content by correlating the strength of the fluorescence emitted

by the oil droplets when excited by laser. Generally, with a smaller oil droplet size (with oil concentration

kept the same), a much larger surface area is available, which would generate more fluorescence. This

phenomenon led to the observed trend of under-estimate with a larger droplet size.

Effect of Flow Velocity

The Canty sensor was a side stream configuration, with a sample ring that used the pressure difference

upstream and downstream of the sampling probe to generate flow through the sensor. It is typically

considered that iso-kinetic flow is desirable for obtaining representative samples, i.e., the velocity in the

sampling probe is the same as the main flow. Since the oil droplets are small and also close in density with

water, the sample can be representative even if the velocity in the probe is much lower than the main

flow. However, there seems to be a lower limit on the main flow velocity, below which the flow into the

sample probe is too lower, causing the fluid into the sample to be no longer representative. This was not

certain though. Further work is likely needed to confirm.



October 19, 2016 48

The Clearview sensor s easu e e ts were not sensitive to the flow velocity. The sensor uses a relatively

la ge / sa pli g tu e to ge e ate the side-stream that the sensor uses. The side stream drains to

atmospheric pressure therefore the flowrate through the sample piping mostly depends on the pressure

at the sampling point, which did not vary significantly during the tests. The imaging operation was

performed with an isolated sample, so was not affected either by the velocity in the main flow.

The Digitrol sensor used a hydrodynamic design combining a Venturi tube profile and an obstacle, located

immediately upstream of the optical window, to generate high velocity and turbulence in the optical

window area to prevent fouling of the window. The design appeared to have a narrow range of main flow

velocity with which it works. If the velocity is too low, the turbulence generated may not be sufficient to

keep the window clean. If the velocity is too high, the pressure may reduce enough for gas bubbles to

fo , o lead to a itatio . The se so s easu e e ts e e highe at /s tha /s flo elo it , possibly due to the accumulation of oil on the window, which increases the scattered light. The

measurements were extremely high at 4 m/s due to dissolved air coming out of solution or even cavitation.

The ProAnalysis sensor s measurement principle was not affected by the flow velocity. It was an inline

probe so the fluid that the sensor measured should be consistent with the main flow. However, when the

test fluids contains solid (in addition to oil), it looks that increasing the velocity increasingly made the

sensor under read. The reason for this is unclear.

Effect of Water Salinity

The Canty sensor had a general trend of over estimating the oil content at a higher water salinity. Videos

images were not always available so it was difficult to fully understand the reason for the increase. A

potential factor was the possibility of the presence of un-dissolved salt particles and impurities in the

ate , hi h ould i ease the se so s eadi gs.

The Clea ie se so s easu e e ts e e severely affected by salinity due to the buoyancy-induced oil

droplet motion during imaging. As discussed for the effects of droplet size, the measurement section in

the prototype was too much off from horizontal, causing excessive buoyancy and overestimating of

droplets. The buoyancy force and the velocity of the droplet are proportional to the difference in density

between the water and oil. As the water changes from seawater to 250,000 mg/L salinity, the density

difference increases by a factor of 3, leading to the overestimate of oil concentration by the same

magnitude.

The effect of salinity on the Digitrol and ProAnalysis sensors looks to be inconclusive. It seems that these

sensors were not affected.

Effect of Solid Particles

For the Canty sensor, presence of solids increased oil concentration readings. Increasing the solid

concentration also slightly increased the oil concentration readings. With the same solid concentration,

the impact of the effect of presence of solids reduced as oil concentration increased. Although the

sensor has the capability of distinguishing solid particles from oil droplets, it cannot completely discount

the solid particles and as a result, it over estimated oil concentration. With an increasing solid

concentration (with oil concentration kept the same), it increasingly over estimated. However as

expected, when oil concentration increased (with solid concentration kept constant), the impact of

solids reduced.



October 19, 2016 49

The Clea ie se so s easu e e ts ge e all edu ed with the presence of solid particles. The

scattering of both excitation and especially fluorescence light by the solid particles led to a reduced

droplet count since the fluorescence of some small droplets may no longer be detectable, and/or

reduced dimensions of the imaged droplets part of the oil on the outer edge of droplets may not have

strong enough fluorescence reaching the detector.

The Digitrol sensor s easu e e ts i eased with the presence of solids and the measurement also

increased with a higher amount of solids present. Being a light scattering based technology, this is

expected as the unit does not currently discriminate between oil droplets and solid particles.

The addition of solids reduced the ProA al sis se so s oil o e t atio easu e e ts, and the higher

the solids concentration the lower the measured oil concentration. P oA al sis p e ious tests did ot show as much an influence of solids, although a small drop (-17%) was also seen with 100 mg/L of solids

added. Presence of solids means that there is less amount of light passing through the sample which

reduces the amount of fluorescence generated. At the same time, with the solid presence, a less

amount of fluorescence light would reach the sensor detector. An increase in the solid concentration

made the situation worse. But increasing the oil concentration with solids concentration kept would

reduce the impact of the solid particles. At a high oil concentration, more fluorescence is generated

from the larger number of oil droplets so the relative impact of solids would reduce.

Effect of Gas Bubbles

The Canty sensor has the capability to distinguish gas bubbles from oil droplets. However, this capability

was not made available during the test program. Consequently, some gas bubbles were clearly counted

as oil droplets, increasing the oil concentration readings when gas bubbles were present.

The Clea ie se so s easu e e ts significantly reduced when gas bubbles were present. It is not

clear at present whether this was due to the presence of gas layer on top which reduced the strength of

both excitation light and fluorescence light, or the cleaning operation was not completely effective and a

fouling layer was built up on the top window by the time the tests with gas bubbles were conducted.

Further analysis is needed to determine which factor, or both, affected the readings.

The Digit ol se so s easu e e ts i eased with the presence of gas bubbles. This is similar to the

effect of solids, the unit does not currently discriminate between oil droplets and gas bubbles, both of

them scatter light leading to a higher concentration measured by the sensor.

The ProA al sis se so s oil o e t atio easu e e ts reduced when gas bubbles were present. Gas

bubbles scatter light, so there is less amount of light passing through the sample which reduced the

amount of fluorescence generated. Less amount of fluorescence light would reach the sensor detector

also due to scattering by gas bubbles. Both led to reduction of fluorescence detected by the sensor and

lower oil concentration readings.

Effect of Oil Density

The Canty sensor s reading in general decreased under the same oil concentration when changing from

a heavier oil to a lighter oil. This is due to the difference in the contrast between oil and water in the

images when oil gravity changes. A possible explanation is that with a heavier and darker crude oil, the

contrast allows the camera to see oil droplets better. With a lighter oil, in particular gas condensate oil,



October 19, 2016 50

it becomes less obvious. Canty commented that they had prepared two calibration files, one for heavy

oil and one for light oil due to the increased contrast of the heavier oil compared to the lighter oil.

However, apparently due to a miscommunication, only one was considered sufficient for the test

program. Nonetheless, the results showed the importance of calibrating the sensor to the specific oil

anticipated to be measured in the water.

The Clea ie se so s easu e e ts increased with lighter oil (higher gravity). The readings were

affected by the density due to buoyancy force similar to that discussed above, which tends to increase

the readings with lighter oil. The readings were also affected by the level of fluorescence if it is

significantly low, such as the 15 API oil. A preliminary study of the oil composition indicates that it has

much less aromatics than the other oil. The much weaker fluorescence can lead to reduced readings

with undetected droplets due to highly pixelated images.

The Digit ol se so s easu e e ts were not very sensitive to oil density changes since the light

scattering characteristics of oil is not very sensitive to the oil types.

The P oA al sis se so s oil o e t atio easu e e ts increased under the same oil concentration

when the oil was changed from lower to higher API (higher to lower density). LIF sensor is based on

measuring polycyclic aromatics hydrocarbons (PAH) that fluoresce. Thus proper calibration against a

specific oil is extremely important as different crude would contain different amount of PAHs that

fluorescence. Although in general, the lighter oil, e.g. condensate oils contains a less amount of PAHs,

thus, one expects to receive less signals and therefore less amount of oil measured. However, the oils

used in the tests seemed to have the reverse trend (see above discussion regarding the Clearview

sensor).

Effect of Temperature

The Canty sensor did not provide readings in the runs with 0.5oC and 3oC water temperature, potentially

due to the hardware not functioning at these low temperatures, or more likely extreme window fouling

by wax or paraffin deposition in these runs. However Canty commented that the system would not be

affected by a low temperature. Therefore fouling was likely the reason for the observed sensor response

in low temperature runs. Between temperature 5 and 65 oC, an increasing readings was found as

temperature increased, from circa 35% underestimate to 135% overestimate. At 90 oC, the reading was

close that at ambient. The change is not understood at present. One possibility may be to do with a

possible change in the base water quality as the water was heated up.

The Clea ie se so s did not provide readings in the runs with 0.5oC and 5oC water temperature,

potentially due to the microscope not functioning in these runs. It seems likely that the air circulation

built in the prototype was not able to keep the protective chamber warm enough for the microscope to

function, since the microscope used in the prototype is typically used in room temperature. The

measurements were not sensitive to temperature changes from 12 to 65 oC. Higher temperature (90 oC)

reduced the sensor readings. In this run, the droplets imaged were generally smaller and fewer than

other runs, suggesting that the reduction in reading may be sampling related (sampling velocity is about

1/4 of main flow) including loss of some bigger droplets to sample piping wall. It is not clear at present

the reason for the sampling issue to occur at the highest temperature run.



October 19, 2016 51

The Digitrol sensor provided readings in each of the temperature runs. The measurements were highest

at the lowest temperature (175% higher than EPA Equivalent), then varied between -19% and 77%

deviation from the EPA Equivalents for the higher temperature. Since the measurement principle is not

sensitive to temperature, it is assumed that potential paraffin formation and deposition on the optical

window at very low temperature might have occurred, which increased the oil concentration measured.

The ProAnalysis sensor provided readings in each of the temperature runs. At temperature below or

above ambient temperature, the sensor consistently under read by about 65% (compared to 17% over

read at ambient), and not seems to be affected by the change in temperature. ProAnalysis commented

that temperature would not influence LIF measurements, and that different temperature settings might

alter droplet size in the test loop. One of the temperature runs (3 oC) had almost 100% variations in the

EPA Equivalent measurements throughout the run. However this seems to be an outlier since the other

EPA Equivalent values were much more stable in other temperature runs, and the values stabilized to 23

– 25 mg/L in the second half of this run. On the other hand, the ambient temperature run was

performed early in the test program while the other temperature runs were conducted several weeks

later. It is worth pointing out that the ProAnalysis sensor measured -56% to -87% lower than EPA

Equivalent in the oil concentration repeat runs toward the end of the test program. Similarly the

measured oil concentration in the repeated oil droplet size runs toward the end of the test program also

had large decrease from the measurements in the initial runs at the beginning of the program.

The efo e it a e easo a l o luded that the se so s easu e e ts e e ot se sitive to

temperature, and that a factor other than temperature (such as sensor shift/drift) was the reason for

the observed complex trends of temperature effect.

Effect of Chemicals

The HEM measurement by the EPA Method 1664B analytical procedure and the EPA Equivalent for the

runs with the Hydrate Inhibitor may not be as reliable as in other runs, as discussed in the bench-scale

test report (Attachment 5). However noticeable trends in sensor responses can still be observed for

qualitatively assessing the effect of hydrate inhibitor on sensor accuracy.

The Canty sensor was affected by all of the four chemicals tested. Corrosion Inhibitor, DeOiler and Scale

inhibitor all increased the amount of oil that the monitor reported compared to baseline performance,

whereas the Hydrate Inhibitor reduced the oil recorded by 90% of the EPA Equivalent. Further study is

needed to understand the effect of chemicals.

The Clearview sensor had an issue that needed minor repair and microscope position re-setting.

Unfortunately data for of these chemical tests were not available due to the sensor being offline for the

repair and re-setting.

The Digitrol sensor was only really affected by the Hydrate Inhibitor, which increased the concentration

the monitor read significantly. The increase in measured oil-in-water by the sensor is partly linked to a

reduction of the oil droplet size when Hydrate Inhibitor is present. With an exception of the Hydrate

Inhibitor run where the oil droplet size was found to be much smaller, for all the rest of chemical runs,

oil droplet size was kept reasonably close to 15 µm. Other chemicals do not seem to affect. It is worth

mentioning that oil droplet size was kept reasonably close to 15 µm in other chemical test runs.



October 19, 2016 52

The ProAnalysis sensor was little affected by the Corrosion and Scale Inhibitor products. The DeOiler and

Hydrate Inhibitor chemicals both decrease the amount of oil that the monitor recorded. The effect of

chemicals looks to be complicated as they can contribute to oil concentration measurement themselves,

and also alter the characteristics of PW. The Hydrate Inhibitor used was partially water soluble. All the

rest of chemicals are completely water soluble. More testing is needed.

6.2.6 Assessment of Sensor Performances

The assessment on inst u e ts pe fo a e he e is a ied out e a i i g follo i g aspe ts:

Accuracy

Repeatability

Fouling mitigation effectiveness

Sensor robustness

Sensitivity to a change in produced water parameters

Accuracy here is defined as the closeness between what the individual instrument measures and the value

of EPA equivalent from the IR. The discussion on repeatability here is based on the repeated runs carried

out in the bench scale testing, i.e. those on oil only (API 30) and oil droplet size effect runs (API 30). Sensor

robustness here is judged on whether or not there was any maintenance or intervention required during

the bench scale testing.

Canty

A good percentage of runs (21.2%) already within ±15% to EPA Equivalent; 58% within ±50%,

16% over ±100%.

Very good repeatability both in terms of the individual results and trends in the repeat test runs.

Fouling mitigation (using jetting) seems to be effective for fouling induced by oil soaking, but it

was not effective for fouling created by grease brush-painted. There was no intervention /

maintenance needed during the trial.

The instrument was found to be sensitive to almost all parameters. However, in most cases,

there was a clear trend. With a clear trend, it is possible to make a correction. Additional

capability is also available, e.g. setting higher resolutions for smaller oil droplets, recognition of

bubbles and types of oils etc.

Overall it is a robust sensor that is not limited to any particular application. It has an excellent

potential for both surface and subsea applications.

Clearview

Difficult to judge accuracy with the originally processed data due to erroneous readings that was

resulted from an overly simplistic imaging processing algorithm.



October 19, 2016 53

Difficult to judge on repeatability, and also on sensitivity towards changes of test parameters.

The data were re-processed post the tests with improved algorithms. With the re-processed

data, the sensor showed clear trends in the effect of most parameters. Clearview has identified

that the effects were primarily caused by two factors: buoyancy driven motion of droplets due

to the non-horizontalness of measurement section, and reduced light strength in some cases.

Fouling mitigation seems to be effective for fouling induced by oil soaking. It was not clear if it

was effective for fouling created by grease brush-painted.

The sensor also required some intervention towards the end of the bench scale testing.

Overall, it is a newly developed sensor which fared well in the bench scale testing.

Digitrol

Digitrol has the highest percentage (23.9%) of runs already within ±15% to the EPA Equivalents;

62% within ±50%, 26% over ±100%.

Poor repeatability in terms of the individual results. However a similar trend was repeated in the

drop size effect runs.

Fouling mitigation (using hydrodynamics) looks to be effective for both fouling mitigation tests

and no intervention was needed during the trial.

The instrument was found to be sensitive to droplet size, solid, gas, velocity as expected.

Overall it is a robust sensor and faired really well in the current testing. The sensor is only

applicable for oil-in-water measurement. It has an excellent potential for both surface and

subsea applications.

ProAnalysis

It is surprising to some extent that it had the lowest percentage (7.1%) of runs already within

±15% to EPA Equivalent; 42% within ±50%, but only 2% over ±100%.

Poor repeatability in terms of the individual results in the API 30 oil repeat test runs, but a

similar trend was repeated in the drop size effect runs. It looks like that the instrument had

drifted during the bench scale testing period.

Fouling mitigation (using ultrasonic) looks to be ineffective from the fouling mitigation tests,

which is unexpected. Previous experience showed that ultrasonic cleaning would work well with

the test conditions that the project had. Further tests may be needed to fully understand the

fouling mitigation effectiveness. No intervention needed during the trial.

The instrument was found to be sensitive to droplet size, API gravity, velocity as expected.



October 19, 2016 54

Overall it is a robust sensor and fared well in the current testing. It is a sensor only applicable for

oil-in-water measurement. It has an excellent potential for surface, however, for subsea

applications, this will depend on having an effective fouling mitigation technology. Ultrasonic

only works at a low to a moderate pressure environment.



October 19, 2016 55

7 TECHNOLOGY READINESS LEVEL

Following on the bench scale testing at Fjords Processing, updated TRLs, following the definitions of API

RP 17N, has been assigned to each of the sensors tested, as shown in Table 9. The justification and note

of caution when using the TRLs are also provided below. Additional discussions can be found in Ref. 4.

Table 9. Technology Readiness Level of Subsea Produced Water Quality Sensors

Existing New

Vendor J.M. Canty Digitrol ProAnalysis Clearview Subsea

Model Loop Flow OIW TOG Argus CLFM

TRL (Note) 3 6 3 3

Note:

The technology readiness level assessment is for subsea application. For surface applications,

the existing technologies (J.M. Canty, Digitrol and ProAnalysis) have commercial models.

7.1 Justification

For the three existing technologies, J.M. Canty, Digitrol and ProAnalysis, there have been no changes to

the TRLs that were assigned to them when the gap analysis was carried out as part of Phase I of the

project. These technologies were already proven for surface applications. However, the current RPSEA

project helped the vendors with existing technologies to further refine their technologies. For example,

for J.M. Canty, a sample loop has been developed and incorporated into their core Inflow microscopy

technology. The bench scale testing provided an opportunity to assess the performance and reaffirm its

TRL status.

As far as subsea applications are concerned, both J. M. Canty and ProAnalysis have made a significant

amount of effort in developing a subsea water quality measurement sensor both within the company

and with a third party operating company. Prototypes from each of the two vendors have been built,

which have been function and performance tested. As for Digitrol, a TRL of 6 was given on the basis that

one of their TOG units had already been developed, qualified and installed at the Marlim field for a

subsea produced water re-injection application, and its performance on produced water discharge has

been bench-scale tested.

For the Clearview Subsea CLFM sensor, a TRL 3 is now given on the basis that a prototype has been built,

and its function and performance have been tested through the bench scale testing at Fjords Processing.

This technology had been newly developed as part of the RPSEA Phase I project and was assessed and

selected for Phase II by the WPG members. Further research and development has been continuing

th oughout the Phase II of the ‘P“EA p oje t. The e h s ale testi g has sho that the CLFM se so s online data contain some obvious errors, which Clearview Subsea confirmed that these were largely due

to image processing issues. Clearview Subsea has since reprocessed the images using an improved

algorithm with very encouraging results. It is recommended that a further bench scale testing be

conducted to validate the improved algorithm that can function properly in an online operation

condition.



October 19, 2016 56

7.2 Note of Cautions

With the exception of the Digitrol TOG, it is understood that reliability tests such as reliability growth

tests, accelerated life tests, have not been performed by the other three vendors.

With regard to prototype being tested to the extent to which application requirements (i.e. subsea) are

met, on temperature and pressure, there are two considerations:

1. Temperature and pressure of produced water (for instrument internal consideration)

2. Temperature and pressure of the sea water (for instrument external conditions)

On the first one, regarding the temperature, the bench scale test does try to cover. One of the test sets

ei g a ied out at Fjo ds P o essi g is the Effe t of Te pe atu e , hi h o e s f o . to 9 oC .

With regard to the pressure, an early discussion with the WPG members concluded that oil and grease

measurements (of all the selected technologies) in principle should not be affected by the produced

water pressure. In another word, if the sensor performs under a lower pressure, it will in theory perform

under a high pressure.

On the second one, it is understood that this would be addressed as the technologies move toward the

TRL 4 qualification, which is beyond the current project. The consensus among the WPG members was

that this would be addressed when marinization is carried out.

It is the WPG e e s ge e al ie that a i izatio is elati el st aightfo a d, it is the o ust performance of the sensors that is most important for the current project to test and find out.

7.3 Other Important Parameters

The e a e othe i po ta t pa a ete s as ell, su h as se so s a u a , hi h are not covered by TRL.

Results from the bench scale testing confirm that improvements will need to be made to each of the

sensors if an accuracy of within ±15% to the EPA 1664 B is to be achieved. Digitrol achieved the highest

with 24% of the test runs meeting the ±15% to the EPA Equivalents, J.M. Canty ±21%, with the other two

less than ±10%.

The bounds of the measurement is another important factor in accuracy. ProAnalysis had the most

measurements within ±100% deviation from the EPA Equivalents, with only 2% are over. J.M. Canty and

Digitrol followed, with respectively 16% and 26% of measurements over ±100% in deviations from EPA

Equivalents.



October 19, 2016 57

8 SYSTEM INTEGRATION, FIELD TESTING AND COMMERCIALIZATION PLAN

8.1 Improvements or Changes to Sensors

Sensor vendors have proposed the following improvements or changes to the sensor design, following a

review of their own test results. The project team has also made recommendations on improvements

and technology developments for the individual sensors. These are shown in Table 11.

Table 10. Vendor Comments on Key Factors Affecting Sensor Performance and Recommended

Improvements

Sensor Key Factors Recommended Improvements

Canty Pixel resolution

Cleaning of optical window

Calibration for the oil used

Calibration for anticipated solids

Use higher magnification lens setting

(0.5 micron pixel scale factor)

A more frequent cleaning cycle than

used in the tests

Clearview

Image processing algorithm: detection

of the top of the fluid and image frames

with error

Horizontalness of Measurement Section

Reliability of microscope components

(Signal strength stability, objective

mover repeatability)

Temperate ratings of microscope

components

Accumulation of oil residue on optical

window during extended service

Loss of laser and fluorescent light

intensity during transmission

Improve detection of the top of the

fluid and error frames

Stricter fabrication tolerance on

Horizontalness of Measurement Section

Perform reliability tests for sensor

components

Select and test microscope components

to meet the required temperature

range

Add automatic adjusting of laser

strength using the signal strength as

input

Digitrol Cleanliness of the window – avoid

window running dry

Velocity of produced water

Whether pressure of produced water is

above a minimum value

Solids and gas bubbles are not

differentiated at present

Potentially using additional windows /

detectors to distinguish interferences

Use procedure to cancel the influence

of solids and gas, if the quantities are

constant, as are expected to be the case

in subsea application

ProAnalysis Oil types and some process parameters

Calibrated for the anticipated oil type

Calibrated on-site according to relevant

process parameters



October 19, 2016 58

Table 11. Recommended Technology Development and Improvements for the Sensors by the Project

Team

Sensor/vendors Technology Development and Improvements

Sensor Specific To All Sensors

Clearview Subsea It is anticipated that both software and hardware are to be

improved. Some of the improvements have already been

incorporated following the bench scale tests as detailed in the early

sections.

TRL 3 - Reliability

tests

Development of

prototypes for TRL 4

to TRL 6 with the

exception of Digitrol

who has already

gone through the

qualification

process previously.

Digitrol Fouling mitigation using hydrodynamic is a good idea, but its

operating envelope looks to be narrow; worth being expanded

One of the key issues is that it is significantly affected by the

presence of solids, gas bubble and changes in the droplet size. The

effects need to be reduced.

J. M. Canty Ca t s se so has ee sho to e o ust a d ge e all repeatable despite the fact that it was found to be affected by most

of the parameters tested. The key improvements should be centered

on how to reduce the effect of solids, gas bubbles, droplet size,

different types of oils and memory effects. In most of the cases,

there has been a clear trend. With the capability available from

Canty, it is believed that most of these can be addressed. Fouling

mitigation using jetting looks to be working and effective with

fouling by crude oil. But a more robust fouling mitigation may be

needed for more difficult fouling.

ProAnalysis ProAnalysis sensor has also been shown to be robust. Key

improvements should be focused on how to reduce the effect of oil

droplet size, make it less sensitive to the types of oils. This is believed

to be achievable with calibration. It is also advisable to look into how

to reduce the effect of the presence of solids and gas bubbles. One

of the key elements that need to be addressed by ProAnalysis for

subsea application is the cleaning. Ultrasonic cleaning which is

currently deployed, works at a low pressure from experience, but it

does not work under an elevated pressure.

8.2 Technology Development Plan by Sensor Vendors

O l o e se so , Digit ol s light s atte i g se so , has ee i teg ated ith a su sea system to date, and

it has been used to monitor the water quality in a subsea produced water re-injection application. The

other sensors tested vary from commercial topside models to an early version of subsea prototype. It

should be noted that a lot of further developments will be needed for each sensor tested if they were to

be used for subsea produced water discharge quality measurement applications.

8.2.1 Digitrol

“ ste i teg atio fo the Digit ol s se so has ee o pleted i the p e ious su sea i stallation.

Future developments required for subsea PWD applications are primarily related to:



October 19, 2016 59

Enhancement of measurement capability to meet the accuracy requirements for PWD

applications;

System integration design and tests if the enhancements lead to changes in the interface

between the sensor and the subsea system;

Laboratory and field testing to confirm both measurement performance and fouling prevention

performance under realistic conditions;

Qualification for use in specific projects.

Regarding measurement performance, Digitrol is aware of that its sensors are severely affected by the

presence of solids and gas bubbles. Digitrol has provided following comments:

Digitrol does not expect gas bubbles to be significant with pressure over 100 bars (1450 psia)

which is typical for subsea applications.

It is possible to use additional windows / detectors to distinguish interferences (such as gas

bubbles and solid particles). Digit ol s own research suggests that it will reduce the influence of

gas bubbles and solids but will not completely solve the issue.

The quantity and size of solids and bubbles are expected to be pretty constant in field

applications, and eventual changes are not anticipated to be large. If the quantity of solids and

gas, as well as pressure are constant, it would be very easy to cancel the influence (similar to the

procedure in a spectrophotometer using blank liquid). Digitrol has been using this procedure on

platforms with success.

Digitrol is developing a new prototype to handle the gas bubbles. It is anticipated that the first step for

the new prototype will be tested imminently.

Digitrol considers that major maintenance, such as eliminating the accumulation of oil deposits or other

fouling after long term use and/or after water quality deviates significantly from the design conditions,

should be handled by through the design and operation of the overall subsea produced water treatment

system. Therefore, it is not envisioned that the sensor will have additional design features to handle

major maintenance.

8.2.2 J.M. Canty

J.M. Ca t s tested sensor is a modified version of their early subsea prototype built and tested in

p e ious JIP s. The modification includes the following two new components which were specifically

developed to suit subsea applications for this project:

Sample ring to allow for a side stream (at-line installation) of produced water taken from the

main pipe, and returned back to same location;

Pumping module for pressurizing the cleaning water.



October 19, 2016 60

The benefit of the at-line design is that the size of the sensor becomes independent of the produced

water pipeline size, which offers many advantages in manufacturing, testing and costs. The particular

design also lends itself to the installation and retrieval process due to its compact size manufactured

with pipe. The sa ple i g a e made for any size flow pipes and can remain permanently

mounted. The sensor could be removed from service without interruption of the main produced water

flow. The system design will likely lend itself to redundancy by duplicate installations at a reasonable

cost, thereby further streamlining the system electronic design to its simplest form.

To progress the sensor to full readiness as a subsea produced water discharge quality monitor, Canty

plans the following steps:

1. Modify the current subsea flow body and housing design to confirm with this improved design

concept.

2. Design a separate power supply cylinder with wet-mate connection capability and a simplified

retrieval process.

3. Add shut-off valves and system separation points for easier installation and retrieval.

4. Integrate the package into subsea platform. This is anticipated to be in co-operation with

owners a d pe the o e s preferences on the parts listed in Items 1-3.

5. Demonstrate full system capabilities with a topside test.

6. Simulate a full depth pressure test.

7. Demonstrate an installation by simulating subsea structure and testing the installation both

above and below water. Mount the instrument/power supply, connect the power/signal cables,

and fully test the instrument function.

8. Select a test site for installation, install a sample ring in flow pipe, install mounting structures,

and install instrument it and connect to the SCM.

8.2.3 ProAnalysis

The tested ProAnalysis sensor is a commercial low-pressure topside model. The company has been

participating in a JIP to develop a subsea produced water sensor, and is preparing an offshore surface

test later this year. As a result ProAnalysis cannot disclose additional information on its plan related to

their subsea technology developments at present.

ProAnalysis believes that anti-fouling is a bigger challenge than marinization for subsea monitors.

8.2.4 Clearview Subsea

The tested Clearview Subsea sensor is the very first prototype of a newly developed OIWM technology

based on CLFM. The design of the tested unit is specific to the bench-scale testing environment

(laboratory), with up to 150 psi water pressure rating and 0 – 100oC water temperature rating (13 - 3oC



October 19, 2016 61

at the microscope components). An air-circulation fan was installed in the sensor to keep the

temperature inside the protective cover within the allowable temperature range for the microscope and

control hardware.

Clearview Subsea plans the following steps to progress the technology to full readiness (TRL 6 – System

Installed) as a subsea produced water discharge quality measurement sensor:

1. Modify the sensor to implement the improvements identified (Section 8.1). Perform another

bench-scale testing to confirm that the sensor s real time measurement performance is

satisfactory with the improvements.

2. Conduct a field trial on an offshore platform where the sensor will be installed at an overboard

discharge location to see how it performs. The field trial will last two to three months during

which it is anticipated that the sensor would have encountered multiple instances of steady-

state and transient operations.

3. Conduct reliability tests of the sensor components and the system. In addition to life time and

MTBF assessments, the components tests will include laser and photon multiplier tube stability,

objective mover endurance and repeatability, and handling of excursions such as produced

water parameters that are either temporarily or for an extended period of time out of design

ranges. A full TRL 3 will be achieved at the end of this step.

4. Build a subsea prototype. Perform environment tests such as hyperbaric submerge tests.

Achieve TRL 4 (Environment Tested).

5. Finalize the sensor design and perform system tests. Test the hardware interfaces and

integration capability into a subsea control system. Test sensor functions in the integrated

system. Achieve TRL 5 (System Tested).

6. Select a subsea test site. Working closely with subsea system providers and operators to identify

a suitable site, install the subsea prototype and test it for an extended period of time. The site

can be a subsea production facility or a subsea test place specifically set up to test underwater /

subsea equipment in a shallow water environment. Achieve TRL 6 (System Installed).

7. Complete the qualification process with an installation at a subsea produced water treatment

and disposal system and operating over an extended period of time, e.g., 3 – 4 years. Evaluate

its reliability and risk of early life failures in the field. Achieve TRL 7 (Field Proven).

8.3 Multi-Sensor System Integration and Field Testing

As can be seen from the above discussions, most providers of the sensors tested in this project have

plans to develop their respective produced water sensor technologies for subsea use. All plans include

offshore topside tests. System integration and a subsea field trial will be required to achieve TRL 6.

Therefore, it will be beneficial from a technology development and acceptance standpoint if multiple

sensors are simultaneously tested when it comes to system integration and field performance testing

and evaluation. Such a program can be much more cost effective and efficient compared to the case

where the system integration and field trials are conducted independently for each of sensors.



October 19, 2016 62

A simultaneous multiple sensor testing program has an added benefit. It allows for evaluating the

potential use of multiple sensors on a single subsea system to increase the confidence level required for

setting off alarm signals for a production system shut down or diverting the produced water flow.

8.4 Additional Considerations

As noted in the Section 7, Digit ol s se so is the o l se so that the reliability tests aspect for subsea

applications has been addressed for. To progress the technologies, this aspect will have to be addressed.

One of the difficulties is that all oil-in-water monitor suppliers and / or developer are relatively small

sized companies, to carry out all the reliability tests (reliability growth tests, accelerated life tests), it is

thought that a significant amount of resources is required. Additionally, moving from TRL 3 to TRL 7,

sensors will have to undergo multiple additional steps per API RP 17N, namely pre-production system

environment test, production system interface tested, production system installed and tested, and field

proven. All these steps require substantial amount of resources, which will unlikely be available from the

vendors alone. Securing support on the resources needed will be one of the key factors in moving the

technologies forward.

For performance assessment, one of the difficulties encountered is the fact that there is no absolute

value in oil-in-water, oil-in-water is a method defined parameter. Currently to assess an online oil-in-

water i st u e t s pe fo a e, o e has to o pa e hat is easu ed the o li e o ito to hat is measured by sampling and analysis using a reference method. The methodology was utilized in the

current project, with the EPA Method 1664 B being the reference method. Achieving the 15% of the EPA

Method B was challenging not only due to the effect of test parameters on sensor measurements, but

also because results obtained by using sampling and analysis of the EPA Method 1664 B have its sizeable

associated uncertainties. Therefore, there is some limitation in bench scale testing where the

performance of online monitoring devices for a method defined parameter such as oil-in-water requires

to be assessed against values from a reference method.

It is understood that EPA is currently developing a protocol that would allow for the acceptance of using

online measurement devices for method dependent parameters such as oil-in-water. It will be a

significant step if such a protocol is developed and available. Indeed this may change how performance

testing and assessment should be conducted in the future for online devices such as oil-in-water

monitors.

Another aspect of the accuracy is the duration of time. The current technical requirements do not

specify the duration of time for evaluating accuracy. The response time required hourly or faster, and

operator specific. The bench-scale testing results were averaged over the run, typically 1 to 2 hours, to

compare with the EPA Equivalents. Achieving 15% accuracy over the short time is challenging, as was

discussed above. On the other hand, regulatory requirements are based on longer time, such as the

daily maximum and monthly average requirements in NPDES General Permit. Over the longer time, the

events with parameter values at which a sensor has high deviations from reference method may only

occur for a small fraction of the total time, thereby not have a significant impact on the overall

comparison. It is recommended that further consideration should be given on whether the accuracy

should be evaluated in the durations that match the regulatory requirements.



October 19, 2016 63

9 CONCLUSIONS

9.1 Technology Readiness Level

The technology readiness levels for subsea produced water quality sensor have been updated for the

four sensors that were bench-scale tested in this project.

Digit ol s TOG light s atte i g se so has been given a TRL 6, based on the fact that one of their TOG

units had already been developed, qualified and installed at the Marlim field for a subsea produced

water re-injection application, and its performance on produced water discharge quality measurement

has been bench-scale tested.

Three other technologies have been progressed to TRL 3 on the basis that prototypes have been built

and that these prototypes have been functions and performance tested. These a e J.M. Ca t s I Flo i os opi i agi g se so , P oA al sis A gus lase i du ed fluo es e e se so a d Clea ie

“u sea s o fo al lase fluo es e e i os op se so . However a note of caution should be made that

for each of the three technologies, reliability tests for the subsea applications are still to be carried out

by the individual sensor providers as part of further developments. As Clea ie “u sea s se so required post-test re-processing of the images to remove erroneous readings that made the originally

processed results not usable for evaluation, it is also recommended that a further bench-scale test be

conducted on the sensor to evaluate the performance of the revised algorithms in an online

environment.

9.2 Technical Requirements on Subsea Produced Water Quality Sensors

A full set of technical requirements for subsea produced water discharge quality monitoring sensors

have been developed. Since there is no regulation at present specifically developed for subsea

discharges, the requirements are based on current regulations for surface discharges. The requirements

reflect the thoughts of industry experts and the feedback from regulatory agencies. It is anticipated that

these requirements will be further revised to cover new environment and operating conditions and to

incorporate fresh input from the regulatory agencies.

The sensors ai function is to measure the oil content in water for regulatory compliance and process

monitoring. Additional functions offered by some sensors, e.g., measuring solids content and particle

size, were not evaluated during the current project. The sensors however will be required to maintain

acceptable accuracy when non-oil constituents of produced water (solids, gas bubbles and chemicals)

are present. The impact of the constituents on the accuracy of oil content measurements were studied

in the bench-scale testing during the project.

The sensors are not required to measure toxicity (since it would be assessed as needed via periodical

retrieval of samples by ROV and testing in surface laboratories), or observe sheen (since it is not

applicable to subsea), or measure produced water flowrate (since the estimate required by NPDES

General Permit can be achieved with flowmeters or pressure sensors which are expected to be available

on subsea produced water treatment systems)



October 19, 2016 64

9.3 Technology Gap Analysis and Ranking of Existing Technologies

A gap analysis was conducted on existing technologies that could be potentially developed for

measuring oil in produced water subsea. These existing technologies were then ranked based on using

factors including how the individual technology meets the technical requirements, where it sits in TRL

table (as defined in the API 17N), and how the vendor is prepared and willing to develop a subsea

produced water discharge sensor. The four highest ranked technologies for further development in the

p oje t e e: Digit ol s light s atte i g se so , J.M. Ca t s i os opi i agi g se so , Ad a ed “e so s lase i du ed fluo es e e se so , a d P oA al sis lase i du ed fluo es e e se so .

9.4 Development of New Sensor Technology

A new technology, confocal laser fluorescence microscopy, was also developed during the project. Proof

of concept study concluded that the technology had the potential to be further developed to become a

robust and accurate subsea PWD sensor since the measurement principle was either not or hardly

affected by changes in most of the produced water parameters. A bench-scale prototype was

constructed and tested during Phase 2 along with three sensors with existing technologies. The tests

confirmed the feasibility of implementing the CLFM measurement principle in an online sensor.

9.5 Subsea PWD Sensor Prototype Designs

The technologies selected for prototype design, construction and bench-scale testing in the current

p oje t e e Digit ol s TOG light s atte i g se so , J.M. Ca t s I Flo i os opi i agi g se so , P oA al sis A gus lase i du ed fluo es e e se so , a d the e se so te h olog f o Clearview

Subsea, confocal laser fluorescence microscopy. Designs of the subsea PWD sensor prototypes were

developed for all the technologies except for ProAnalysis, which provided a commercial topsides model

for testing. The designs followed a design basis that addressed the technical requirements for the

sensors defined earlier in the project. Two designs were developed for each technology using the same

measurement principle, one for the subsea environment and one for bench-scale testing environment.

Design reviews were conducted with the WPG and confirmed that the designs were suitable for the

subsea and bench-scale testing applications.

9.6 Bench-Scale Testing of Subsea PWD Sensor Prototypes

Bench-scale testing has been successfully conducted using a once-through seawater flow loop on four

sensors: Digit ol s TOG, J.M. Ca t s I Flo , P oA al sis A gus a d Clea ie “u sea s CLFM. The

effects of the following parameters o the se so s accuracy were tested: oil droplet size, velocity,

salinity, solids, gas bubbles, oil density, chemicals, and temperature. The accuracy of the sensors were

evaluated in terms of their deviations from the amount of hexane extracted materials measured by the

EPA Method 1664B analytical procedures, as represented by the EPA Equivalent values correlated from

infrared analysis. The se so s ope atio pe fo a es e e e aluated ith e o tests to dete i e the se so s a ilit a d p o pt ess to etu to o al ope atio afte a high oil-concentration upset



October 19, 2016 65

event, and also with fouli g tests to dete i e the se so s a ilit to e ediate ode ate a d e t e e fouling that may occur in a subsea environment.



the sensors. When the results from the current project are used in the evaluation of sensor

technologies, it is important that the use is within the context of specific application, since the

parameter ranges may be smaller and the number of key parameters may be fewer than those tested.

On sensor accuracy, the tests showed that deviations of less than 15% from EPA Equivalent was

achievable in some of the tests, but none of the sensors was able to achieve this target for the full range

of a pa a ete . The se so s easu e e ts e e affe ted ea h pa a ete . Ea h se so as a le to achieve deviations within 50% of EPA Equivalent for some parameters, but has two or more

parameters for which it has much larger deviations. The sensors were affected differently by the

parameter changes. Most of the effects were consistent with expectations from the measurement

principles, while there remain some effects for which further studies would be required to better

understand.

The sensors were all able to return to normal operation within about 30 minutes after the high

o e t atio e e t has passed. Digit ol s lea i g e ha is o ked ell fo oth fouli g tests. The othe se so s lea i g e hanisms worked well for the moderate fouling case of oil soaking, but were

not able to clean the severe fouling case of manually applied grease.

The bench-scale tests showed that the existing technology sensors (i.e. Digitrol, Canty and ProAnalysis)

were robust in operation, and had good and acceptable accuracies under test conditions that are similar

to their calibration conditions. The sensors have excellent potential for surface and subsea applications.

For the ProAnalysis sensor, it is recognized that successful subsea applications will also depend on

having an effective fouling mitigation technology that would work for the subsea pressure.

The bench-scale tests also showed that the new technology sensor (the CLFM sensor from Clearview

Subsea) fared well in the bench scale testing. Although it was difficult to judge the accuracy with the

originally processed data due to erroneous readings resulted from imaging processing algorithm issues,

the i agi g ope atio o ked as e pe ted. The se so s esults after improvement in image processing

algorithm were comparable with existing technology sensors for most parameters, and were one of the

best performing sensors coping with oil droplet size changes. The tests also identified several major

improvements in software, sensor construction and hardware reliability that would be required to

further progress toward subsea applications.

9.7 System Integration, Field Testing and Commercialization Plan

Additional development for all sensor tested will be required for progressing toward subsea produced

water discharge quality measurement applications. Sensor vendors have proposed areas of further

developments, and the project has also made recommendations on the development the each of the

tested sensors, for achieving a higher accuracy and/or reducing the effect of parameters. For all sensors

other than the Digitrol sensor, one of the steps required prior to achieving TRL 4 is the reliability testing.



October 19, 2016 66

Criteria for evaluating and accepting online sensors for Oil and Grease monitoring for compliance is

another important subject for further development and commercialization. EPA s de elop e t of a

protocol for approving alternative methods for Method Defined Parameters, such as Oil and Grease, will

be a significant step forward toward the use of subsea produced water discharge sensors.



October 19, 2016 67

10 RECOMMENDATIONS

It is recommended that:

The oil and gas industry continues working with regulatory agencies, and obtains further

guidance on how to accept using an online monitoring device to measure oil and grease for the

purpose of regulatory compliance reporting. It is understood that the EPA is developing a

protocol for accepting the use of sensors for Method Defined Parameters such as Oil and

Grease.

An industry program be initiated to develop recommended practices for use of inline and online

oil-in-water quality measurement sensors and then continue to update it.

Different approaches be explored for the government(s) and the industry to support the

resource needed in the further developing subsea produced water discharge sensors.

Multiple sensors be simultaneously tested for system integration and field trial after the sensors

have reached TRL 4.

Further consideration be given on whether the accuracy of the sensors, as deviations from EPA

Method 1664B or other reference methods, should be evaluated in the durations that match the

regulatory requirements, such as daily and monthly averages. Having the matching durations

may make the sensor performance evaluation more directly associated with the anticipated

online performance, since it takes into account both the parameter effect on sensor accuracy

and the likelihood of the parameter value.



October 19, 2016 68

REFERENCES

[1] Subsea Produced Water Discharge Sensor Technical Requirements Final Report, RPSEA

Document No. 12121.6301.03.01, June 2015.

[2] Technical Gap Analysis Final Report (Phase 1 Final Report), RPSEA Document No.

12121.6301.03.Final1, Revision A, October, 2016.

[3] Subsea Produced Water Discharge Sensor Design Report, RPSEA Document No.

12121.6301.03.Final2.1, January 2016.

[4] Subsea Produced Water Discharge Sensor Lab Test Results and Recommendations Final Report,

RPSEA Document No. 12121.6301.03.Final3, October 2016.



October 19, 2016 A-1

ATTACHMENT 1. RESEARCH ON EPA METHOD 1664 AND CONFOCAL IMAGING

Phase 2 Research on Oil-Water (OiW) Measurement with Imaging Analysis Method

Final Research Report

September 2016

Introduction

This report details the findings for Task 16 of Phase 2 of the project. The overall aim of the task

is to understand the characteristics of oil droplets and solid particles in the produced water at the

discharge point, and the impact of the characteristics on imaging analysis methods of

measurement.

Objective

The specific objective is:

Determining the characteristics of oil droplets and solid particles in the produced water at

the discharge point, and the impact of the characteristics on imaging analysis methods of

measurement.

Methodology (note that Activities 1 and 2 were completed in Task 9 of Phase 1 of the

project)

Activity 3. Bench-scale analysis of treated produced water

The main goal of this activity is to study 3 - 5 samples of produced water. For the purposes of this

activity, actual produced water and synthetic produced water prepared from crude oil was used to

prepare study samples as described below. Additionally, three different oils with different

densities (˚API) provided by Clearview Subsea, Inc. were used.

Actual Produced Water

Produced water samples were provided by Clearview Subsea in three painter’s buckets that were

labeled “clean”, “medium” and “dirty” (see Figure 1 and Figure 2). Samples from each type of

produced water were prepared in the same manner. Samples were prepared by vigorously shaking

the painter’s bucket of produced water in a circular motion on a steady surface for three minutes

to incorporate the contents of the bucket. An IKA T 18 digital Ultra-Turrax disperser (see Figure

1) was then placed into the bucket at 16 x1000 RPM for three minutes in an effort to obtain

homogeneity throughout the bucket. After the produced water was shaken and dispersed, a

volume of produced water was collected in a 1000 mL beaker while the disperser was still

operating in the bucket as shown in Figure 1. Approximately 200 mL of produced water was

poured from the beaker into each sample bottle (amber glass bottles with PTFE lined caps, Fisher

Scientific), and this step was repeated until each bottle was filled to the 950 mL line. It is noted

that while every effort was made to obtain well mixed and homogenized produced water samples

from the three buckets (clean, medium, and dirty), residual oil traces were observed to be

adhering to the interior surfaces of the bucket and the glass beakers used in sample preparation.

Therefore, the measured concentrations may be lower than would be expected.

2

Figure 1. Produced water sample preparation

Figure 2. Actual produced water samples (left to right: clean, medium, and dirty). Note

the oil residue that is present at the water surface along the bucket interior in the

medium and dirty buckets

Synthetic Produced Water using Crude Oil

The density of the crude oil was determined prior to the preparation of the synthetic produced

water. A volume of 2 mL of crude oil was transferred into a pre-weighed 2 mL volumetric flask.

The mass of oil with the volumetric flask was recorded and subtracted from the mass of the

volumetric flask. Crude oil density was determined using the determined volume and mass. The

experiment was repeated three times. The average and standard deviations for the triplicate

measurements were calculated with excel. Mass measurements and density calculations for the

crude oil are shown in Table 0A.

3

Table 0A. Density calculations for crude oil

Trial Mass (g) Density (g/mL)

1 1.823 0.911

2 1.798 0.899

3 1.826 0.913

Average 1.816 0.908

Standard Deviation 0.015 0.0075

All synthetic produced water samples were prepared by adding a known mass of crude oil using a

weight by difference technique to 950mL Millipore water was added to achieve concentrations of

25 and 50 mg/L as demonstrated in the schematic provided in Figure 2A (A concentration of 100

mg/L was also prepared for confocal analyses). For each concentration investigated in the study,

the same synthetic produced water concentration was prepared and analyzed three times to take

into consideration the effect of potential variability on sample preparation and on the analytical

methods used for the oil analyses. The raw samples were vigorously mixed by a disperser (T 18

Digital ULTRA-TURRAX Disperser, IKA INDIA) at 10,000 rpm for 3 minutes for confocal

analyses.

Figure 2A. Schematic of weight by difference sample preparation technique

API 40, 30, and 20˚ Crude Oils The different API oil samples were provided by Clearview Subsea, Inc. and were used in the

study as described below. The density of the three oils was determined prior to the preparation of

the synthetic produced water. A volume of 2 mL of each crude oil was transferred into a pre-

weighed 2 mL volumetric flask. The mass of oil with the volumetric flask was recorded and

subtracted from the mass of the volumetric flask. The density of the crude oil was calculated

using the determined volume and mass. The experiment was repeated three times. The average

and standard deviation for the triplicate measurements were calculated with excel. Mass

measurements and density calculations for API 40, and 20˚ crude oil samples are shown in Table

0B and Table 0C, respectively.

Pasteur pipet

Drop crude oil into

sample bottle

Sample bottle

Crude oil drops

Balance

Vial

Crude oil

4

Table 0B. Density calculations for API 40˚ crude oil

Trial Mass (mg) Density (g/mL)

1 1.704 0.852

2 1.667 0.834

3 1.687 0.843

Average 1.686 0.843


Table 0C. Density calculations for API 20˚ crude oil

Trial Mass (mg) Density (g/mL)

1 1.802 0.901

2 1.945 0.973

3 1.809 0.905

Average 1.852 0.926


Activity 4. Bench scale analysis of treated produced water

The main goal of this activity was to assess the efficacy of confocal microscopy to detect oil in

treated produced water, and to compare the confocal microscopy results to EPA Method 1664

(also referred to as EPA1664 in this document). Variables that were studied in this activity

include environmental factors (pH, temperature, and salinity) and the effect of particles on results

from confocal microscopy (CLFM) and EPA 1664 method.

Initial Precision and Recovery (IPR) and Ongoing Precision and Recovery (OPR) for EPA

1664 EPA 1664 requirements include establishing the ability to generate acceptable precision and

accuracy by obtaining a percent recovery of 83–101% and standard deviation of ≤ 11% of the hexane extractable material (HEM) in four samples of the precision and recovery (PAR) standard.

A mean % recovery of 87.14% and a standard deviation of 4.24% was achieved for the IPR as

shown in Table 0D. An ongoing precision and recovery (OPR) test was completed to ensure

consistency in the method and demonstrate compliance with quality assurance/quality control

(QA/QC) standards determined by EPA 1664. A mean % recovery of 89.2% and a standard

deviation of 2.75% were achieved for the OPR as shown in Table 0E. Results from both the IPR

and OPR experiments are displayed in Figure 2B. A matrix spike (MS) and blank were used at a

frequency of ≥5% to monitor ongoing precision and recovery (OPR) and possible contamination

of samples.

Table 0D. Recovery of initial precision and recovery (IPR) samples using EPA 1664

Mass expected to be recovered: 39.76 mg

Sample Mass Recovered

(mg)

Percent Recovery

(%)

1 33.0 82.99

2 34.2 86.01

3 34.4 86.51

4 37.0 93.5

Average 34.65 87.14

Standard Deviation 4.24

5

Table 0E. Recovery of ongoing precision and recovery (OPR) samples using EPA 1664

Mass expected to be recovered: 40.50 mg

Sample Mass Recovered

(mg)

Percent Recovery

(%)

1 35.9 88.64

2 34.8 85.93

3 36.3 89.63

4 37.5 92.59

Average 36.13 89.2

Standard Deviation 2.75

IPR setup is illustrated in the figures below. The separatory funnels containing IPR samples after

extraction are shown in Figure2C. Sample bottles, beakers containing funnels, filter paper, and

sodium sulfate (NaSO4) for drying the extract, and boiling flasks containing boiling chips for

HEM collection are shown in Figure2D. The distillation set up is shown in Figure2E and a close-

up image of the boiling flasks is shown in Figure2F to demonstrate maintenance of water bath

temperature (85˚C) and vapor temperature (69˚C) during distillation. It is observed in Figure 2G

that the HEM is solid and appears to have a crystal pattern because the stearic acid is solid at

room temperature.

6

Figure 2B. Initial and Ongoing Precision and Recovery for EPA 1664

Figure 2C. Extraction set up

Figure 2D. Experimental setup in chemical hood

0%

20%

40%

60%

80%

100%

Sample 1 Sample 2 Sample 3 Sample 4

Per

cen

t R

eco

ver

y (

%)

IPR OPR EPA Acceptable % Recovery (83-101%)

7

Figure 2E. Distillation set up

Figure 2F. Monitoring water temperature (85˚C) and vapor temperature (69˚C) during distillation

Figure 2G. HEM in IPR samples

Precision and Recovery for EPA 1664 using Tridecane and Mineral Oil Recovery of tridecane was 44.21 ± 24.7% and 65.61 ± 1.59% for 25 and 50 mg/L samples,

respectively as shown in Table 1A. Results for triplicate 25 and 50 mg/L samples are shown in

Table 1A-1 and Table 1A-2, respectively. These recoveries were less than acceptable by EPA

standards (83–101%) possibly because tridecane is a light-oil (C13, density = 0.756 g/mL) and

EPA1664 is intended to quantify oils in the C10 - C40 range and has been known to provide low

8

recovery of hydrocarbons in the C10 - C12 range. The recovery and standard deviation of

tridecane improves with higher concentrations, but tridecane is not an ideal oil to be used for

modeling produced water and is not representative of actual produced water. Mineral oil is a light

oil, but with higher density ( = 0.836 g/mL) than tridecane and generates more acceptable

recoveries. Mineral oil was recovered at 95.86±5.36 and 96.77±5.9% for 25 and 50 mg/L

samples, respectively as shown in Table 1A and results for triplicate 25 and 50 mg/L samples are

shown in Table 1A-3 and Table 1A-4, respectively.

Table 1A. Recovery of tridecane and mineral oil using EPA1664

Oil Type Prepared

Concentration

(mg/L)

Measured

Concentration

(mg/L)

Recovery

(%)

Standard

Deviation

(%)

Tridecane 25 11.1 44.21 24.7

Tridecane 50 32.8 65.61 1.59

Mineral Oil 25 24 95.86 5.36

Mineral Oil 50 48.3 96.77 5.9

Table 1A-1. Recovery of 25 mg/L tridecane using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 23.8 16.6 69.89 25 17.5

2 23.8 10 42.11 25 10.5

3 23.8 4.9 20.63 25 5.2

Average 23.8 10.5 44.21 25 11.1

Std Dev 24.70 6.17

Table 1A-2. Recovery of 50 mg/L tridecane using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 47.5 30.3 63.79 50 31.9

2 47.5 31.5 66.32 50 33.2

3 47.5 31.7 66.74 50 33.4

Average 47.5 31.2 65.61 50 32.8

Std Dev 1.59 0.80

Table 1A-3. Recovery of 25 mg/L mineral oil using EPA 1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 23.8 21.3 89.68 25 22.4

2 23.8 23.4 98.53 25 24.6

3 23.8 23.6 99.37 25 24.8

Average 23.8 22.8 95.86 25 24.0

Std Dev 5.36 1.34

9

Table 1A-4. Recovery of 50 mg/L mineral oil using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 47.5 48.8 102.74 50 51.4

2 47.5 45.6 96.63 50 48.0

3 47.5 43.2 90.95 50 45.5

Average 47.5 45.9 96.77 50 48.3

Std Dev 5.90 2.96

Results

Confocal Analyses for Actual Produced Water

Results with the confocal method indicated that the oil concentrations were 4.88±1.57 ppm,

33.27±11.74 ppm, and 80.06±16.07 ppm for the clean, medium, and dirty samples, respectively,

as shown in Table 1B and Table 1C below.

The values showed that the CLFM method resulted in oil concentrations that were comparable to

the EPA standard method. However, the relative standard deviation for CLFM method, which

generally indicates the precision of a determination, was 1% to 32% higher than the EPA1664

method. As seen in Table 1B, standard deviations of the oil and grease quantification achieved

from clean and dirty produced water were similar for both methods. However, a 32% higher

standard deviation was observed from CLFM measurements for the medium produced water

compared to EPA1664. There are several possible reasons contributing to this large standard

deviation for the medium produced water sample. One potential cause could be the difficult

homogenization of the sample. As reported by Romero et al., oil and grease tend to remain on the

surface of the water, and even if the sample is dispersed vigorously, it will still be difficult to get

representative samples for analysis when only small volumes are needed for the experiments [1].

In the case of the CLFM method, not only the homogeneity of the sample, but also the physical-

chemical properties of the samples could have affected the oil measurements. For instance, the

medium produced water contained different concentrations of salts and organic matters compared

to the clean and dirty produced water samples. This might have affected the interfacial tension

between oil and water, leading to faster oil droplets coalescence. It has been reported that the oil

composition has great influence on the interfacial tension. Gawel et al. also reported that rupture

time for coalescence become shorter when asphaltene content and viscosity decreases [2].

Therefore, the shorter coalescence time might have caused the variance in fluorescence detection,

leading to larger standard deviations.

In addition to the type of oil present, the microscope settings could have also introduced errors in

the CLFM quantification. During the sample detection procedure, it is important to use excitation

wavelengths that will optimally excite the fluorophore and maximize the detection of as many

emission photons as possible. Therefore, it is important to compare the spectra of the target

fluorophores to the spectra of the fluorescence filter sets to ensure the use of correct wavelengths

for the detection of the emission light. It is important, however, to keep in mind that the complex

chemical composition of crude oil usually prevents the resolution of a specific chemical

component in terms of individual emission parameters. In other words, some components in the

produced water might not fluoresce or are not detected in a certain wavelength used during the

CLFM analysis; hence, these components will be not taken into account in the sample analysis

and will lead to an underestimation of the oil content in the water. The heterogeneous

10

composition and different fluorescence fingerprint of the oil components present in the produced

water could have caused the large standard deviations observed during the CLFM measurements.

Table1B. Confocal analysis results for actual produced water

Produced water Sample Measured

Concentration (mg/L)

Standard Deviation

Clean 4.88 1.57

Medium 31.38 11.74

Dirty 80.06 16.07

Table 1C. Confocal analysis data for different stacks and different actual produced water

Clean (mg/L) Medium (mg/L) Dirty (mg/L)

Stack 1 5.95 31.94 75.88

Stack 2 5.62 33.2 63.64

Stack 3 3.07 20.8 64.63

Stack 4 4.12 47.93 63.54

Stack 5 7.55 45.1 75.2

Stack 6 4.58 32.7 83.1

Stack 7 3.88 13.35 88.9

Stack 8 6.33 20.72 105

Stack 9 2.96 40.65 102.57

EPA1664 for Actual Produced Water

The results for actual produced water obtained by using EPA1664 were slightly higher than the

CLFM method, with the exception of the medium sample. Oil and grease in actual produced

water using EPA1664 was measured at 6.56±0.78 mg/L, 28.53±0.28 mg/L, and 86.8±17.09 mg/L

for clean, medium, and dirty samples, respectively as shown in Table 2. Results for triplicate

clean, medium, and dirty samples are shown in Table 2A, Table 2B, and Table 2C, respectively.

An explanation for why the measured oil concentration was higher when using the EPA 1664

method, especially in the dirty produced water samples, could be due to the presence of a

significantly greater amount of alkanes (linear, straight-chain hydrocarbons) within the carbon

range C10 - 40, which are quantifiable by EPA1664 but not by CLFM because they are not

aromatic compounds and therefore, do not fluoresce. As seen in Figure 2H, the standard deviation

(stdev) when using EPA1664 was less than 1% for the clean and medium produced water

samples, but nearly 18% for the dirty produced water sample, which is unacceptable by EPA1664

guidelines. The standard deviation when using the EPA 1664 method was significantly less than

that when using the CLFM method for the clean and medium produced water samples, but

slightly greater for the dirty sample indicating that the dirty produced water sample was

challenging to both methods.

Table 2. Recovery of actual produced water using EPA1664

Produced Water Type Measured Concentration

(mg/L)

Standard Deviation

(mg/L)

Clean 6.56 0.78

Medium 28.53 0.28

Dirty 86.8 17.09

11

Table 2A. Recovery of clean produced water using EPA1664

Sample Measured Concentration

(mg/L)

Standard Deviation

(mg/L)

1 5.4 5.68

2 6.8 7.16

3 6.5 6.84

Average 6.23 6.56


Table 2B. Recovery of medium produced water using EPA1664


(mg/L)

Standard Deviation

(mg/L)

1 27.3 28.74

2 26.8 28.21

3 27.2 28.63

Average 27.1 28.53


Table 2C. Recovery of dirty produced water using EPA1664


(mg/L)

Standard Deviation

(mg/L)

1 69.5 72.4

2 78.2 82.32

3 100.4 105.68

Average 82.7 86.8


Figure 2H. Measured concentration for actual produced water samples

0

20

40

60

80

100

120

clean medium dirty

Me

asu

red

Co

nc

en

tra

tio

n (

mg

/L

)

EPA 1664 Confocal

12

Confocal Analyses for Synthetic Produced Water using Crude Oil

After mixing the synthetic produced water samples as described above in the Methodology

section, 1 mL of each sample was taken from the sample jar and injected into a flow cell (µ-Slide

I 0.8 Luer Collagen IV: #1.5 polymer coverslip, sterilized, ibidi). The flow cell with produced

water was placed on the top of a sample holder and observed using CLFM (Leica DM2500B

SPE, Lasertechnik, Heidelberg, Germany). The produced water oil droplets were examined using

an objective lens of 10X magnification. The fluorescence images were taken using an emission

wavelength of 488 nm with laser intensity at 4-6.5% to visualize the oil droplets. In all

experiments, three stack images of each sample were acquired at random positions in the flow

cell at 4 µm intervals from the top to the bottom of the cell. The total thickness of the stacked

image was 500 µm with an acquisition time of 2.45 min. The same procedure was repeated three

times for each sample. A total of nine stacks for each sample were used for statistical analysis.

The three dimensional stacks of oil droplets were saved as raw images and imported into Matlab

to calculate the concentration of oil content. The oil droplet pixels were separated from

background pixels by global thresholding, which produced binary images with white oil droplets

on a black background. The total sample volume was determined by multiplying the image area

(width by length) by the focal depth, while the oil content concentrations were calculated using

the volume of white pixels showed in the images.

Each set of experiments for EPA1664 were carried out in triplicate, while nine images were taken

and analyzed with CLFM methods for each triplicate sample. For all results, average and standard

deviations were calculated in excel.

One of the most critical steps in this analysis is the determination of an appropriate threshold

value that will eliminate the background fluorescence, but at the same time will allow the

identification of the oil droplets in a sample. Currently, user-defined global thresholding is the

most common approach for binarization CLFM images. Generally, the user selects a global

threshold value, such that the individual image pixels are identified by the method algorithm as

object pixels whenever their intensity is greater than the selected threshold. Whenever, the pixels

in the image are below the global threshold value, these pixels will be considered as background

pixels and will be disregarded from the analysis. In the gray to binary image transformation used

in the study, several threshold values were investigated to determine the optimum global

threshold for oil quantification in synthetic produced water samples.

In this investigation, the different threshold values replaced pixels in an image with a black pixel

whenever the image intensity (I i,j) was less than the threshold value T (I i,j < T), or whenever a

white pixel of the image intensity was greater than that value. The correct choice of threshold is

crucial since further processing and analysis of the distinct oil droplets depends entirely on the

quality of the segmentation. For instance, very low threshold values can result in background

pixels being included in the analysis, while very high thresholds may lead to low-intensity signals

being discarded. This would have a direct impact on the quantification of the oil droplets, since

very low and very high thresholds can lead to over- and underestimation, respectively, of oil

droplets in the samples.

The results of the threshold analyses with 0.3 and 0.4 thresholds (Figure 3) showed that the

samples of synthetic produced water with known concentrations of oil ranging between 25 and

100 ppm overestimated the oil concentrations in the samples. The overestimated values resulted

13

from the algorithm considering some background pixel signals as data. The example images after

threshold algorithm are shown in Figure 4. On the other hand, the threshold 0.6 showed lower

overall oil concentration values than the expected concentration values in the prepared synthetic

produced water samples. The reason is because some low intensity pixel signals coming from the

oil droplets were classified as background, and therefore were excluded from the concentration

estimation. Based on the results, the best threshold value was determined to be 0.5 for the

synthetic produced water using crude oil.

0

50

100

150

200

250

300

350

100 50

Estim

ate

d C

on

ce

ntr

atio

n (

pp

m)

threshold 0.3

threshold 0.4

threshold 0.5

threshold 0.6

25

Prepared Concentration (ppm)

prepared concentration

Figure 3. Measured synthetic produced water concentration with different threshold

Figure 4. Images before and after different threshold values

14

EPA1664 for Synthetic Produced Water using Crude Oil

In general, the results for synthetic produced water generated by EPA1664 were slightly less than

the CLFM method, but the recovery and standard deviation were within acceptable ranges for

both prepared concentrations. Oil and grease in synthetic produced water prepared with crude oil

was recovered at an average of 87±2.7% for 25 mg/L samples and 89±5.5% for 50 mg/L samples

using EPA1664 as shown in Table 3. Results for triplicate 25 mg/L and 50 mg/L synthetic

produced water samples with crude oil are shown in Table 3A and Table 3B, respectively. As

shown in Figure 4A, the standard deviation obtained using EPA1664 was similar to that obtained

when using the CLFM method, but based on the standard deviations, it can be inferred that the

EPA1664 method was more sensitive for higher concentrations (above 50 mg/L).

Table 3. Recovery of synthetic produced with crude oil using EPA1664

Oil Type Prepared

Concentration

(mg/L)

Measured

Concentration

(mg/L)

Recovery

(%)

Standard

Deviation

(%)

Crude 24.4 21.3 87.06 2.67

Crude 49.3 43.5 88.84 5.51

Table 3A. Recovery of 25 mg/L synthetic produced water with crude oil using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 23.1 19.4 83.98 24.3 20.4

2 23.7 21 88.61 24.9 22.1

3 22.8 20.2 88.60 24.0 21.3

Average 23.2 20.2 87.06 24.4 21.3

Std Dev 2.67 0.84

Table 3B. Recovery of 50 mg/L synthetic produced water with crude oil using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 46.2 41.3 89.39 48.6 43.5

2 48.8 45.5 94.06 51.4 47.9

3 45.5 37.3 83.08 47.9 39.3

Average 46.8 41.4 88.84 49.3 43.5

Std Dev 5.51 4.32

15

Figure 4A. Recovery for synthetic produced water prepared with crude oil. Red dashed line is

theoretical 100% recovery (1:1)

Confocal Analyses for API oils

The American Petroleum Institute (API) uses the unitless API gravity in place of density to

compare relative densities of different crude oils. The range in density is due to the concentrations

of the various components of crude oil. For example, if a given oil has a higher concentration of

aromatics than paraffins, it will most likely be denser due to the greater number of heavier

aromatic molecules. Crude oil is classified by API using a formula that compares the density of

water to oil density. The formula for the API gravity can be expressed as:

API = (141.5 / specific gravity) – 131.5 (1)

In this study, four oils with different API gravities are compared to determine the accuracy and

precision of the CLFM measurement. The results of the confocal analyses for the API oils

ranging between 21 and 37 is shown in Table 4A and Table 4B. As shown in Table 4A,

increasing standard deviation values of synthetic produced water with oil samples of lower API to

higher API can be observed, indicating the decrease in precision in data determination with oils of

higher API gravity.

The change in the standard deviation of CLFM measurement for different API oils could be

attributed to the diffusion rate of oil. As described by Hamoda and co-workers, an equation was

developed to express the effects of the API gravity for crude oil, temperature (T), and salinity (e)

on the mass transfer coefficient K [3, 4].

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40 50

Oil

an

d G

rea

se R

eco

ver

ed (

mg

/L)

Oil and Grease Added to Sample (mg/L)

EPA 1664

Confocal

16

K = 1.68 x 10‐5 (API)1.253(T)1.80 e0.1441 (2)

The mass transfer coefficient is a diffusion rate constant that relates to the mass transfer rate,

mass transfer area, and concentration changes as driving forces. Thus, with the increasing mass

transfer coefficient, oil droplets will move faster and collide more frequently, leading to increased

oil droplet coalescence. These parameters could speed up oil droplets movement and decrease the

stability of oil/water emulsion, and result in bigger standard deviations.

Additionally, the mass transfer rate was reported to increase the evaporation rate during the

mixing systems:

E = K C Tu S (3)

E is the evaporation rate in mass per unit area, K is the mass transfer rate of the evaporating

liquid, sometimes denoted as kg (gas phase mass transfer coefficient, which may incorporate

some of other parameters noted here), C is the concentration (mass) of the evaporating fluid as a

mass per volume, Tu is a factor characterizing the relative intensity of turbulence, and S is a

factor related to the saturation of the boundary layer above the evaporating liquid [5]. Therefore,

taking into consideration that more oil components would be lost during the evaporation process,

it is reasonable to observe a higher standard deviation for the CLFM measurement for higher API

gravities.

In addition to the chemical and physical properties of oil, the excitation wavelength employed in

the study will also affect the fluorescence of the oil. In general, light oils (high API gravities) will

have narrow, intense emission bands with small Stokes shifts; while heavy oils (low API

gravities) tend to have broad, less intense bands with greater Stokes shifts. This is due to the high

concentration of fluorophores present in the heavy oils, which in turn leads to a high rate of

collisional energy transfer, yielding a red shift in the emission spectrum. Heavy oils also tend to

contain appreciable amounts of chemical compounds that may quench fluorescence non-

radiatively, resulting in low fluorescence intensities. Conversely, light oils (high API gravity),

with more dilute fluorophore concentrations, have reduced rates of energy transfer, and thus a

narrower emission [6]. The difference on the fluorescence intensity of different API gravities

could also cause the variance on the CLFM detection.

Table 4A. Confocal analysis results for different API oils

25 ppm 50 ppm

Density

(API)

Measured

Concentration

(mg/L)

Standard

Deviation

%

Recovery

%

stdv

Measured

Concentration

(mg/L)

Standard

Deviation

%

Recovery

%

stdv

37.3 22.62 7.77 90.48 31 44.72 14.41 89.44 28.82

36.3 19.84 7.75 79.36 31 51.7 13.69 103.4 27.38

24.3 24.17 1.92 96.68 7.68 45.88 12.36 91.78 24.72

21.3 24.07 3.64 96.28 14 56.69 9.19 113.38 18.38

17

Table 4B. Confocal analysis data for different stacks and different API Oils

Density API

37.3

API

36.3

API

24.3

API

21.3

API

37.3

API

36.3

API

24.3

API

21.3

Stack 1 13.88 12.15 21.97 19.2 25.03 34.57 36.21 43.42

Stack 2 11.38 12.23 25.49 21.46 29.07 34.02 38.35 46.7

Stack 3 14.11 14.38 25.07 21.65 30.02 46.12 58.77 48.75

Stack 4 24.03 15.56 22.31 23.043 44.65 46.56 38.1 57.53

Stack 5 23.57 17.21 24.13 23.38 46.1 46.61 42.19 57.56

Stack 6 24.01 18.32 23.12 24.62 46.87 54.3 37.8 57.9

Stack 7 31.19 25.32 27.26 27.11 53.9 67.03 55.39 59.4

Stack 8 30.25 30.59 22.06 31.38 60.09 67.96 36.1 69.32

Stack 9 31.19 32.5 26.04 24.8 66.77 69.02 69.98 69.64

EPA1664 for API oils

Oil and grease was recovered at 61-66%, 60-64%, and 77-86% for API 40, 30, and 20˚ oils, respectively, as shown in Table 4C. Results for triplicate 25 mg/L synthetic produced water with

API 40, 30, and 20˚ oils are shown in Table 4D, Table 4E, and Table 4F, respectively. Results for

50 mg/L synthetic produced water with API 40, 30, and 20˚ oils are shown in Table 4G, Table

4H, and Table 4I, respectively. Results obtained from the EPA 1664 method indicate a trend that

higher density oils are better recovered than low density oils as observed in Figure 4B by

comparison of results with the theoretical 100% recovery (1:1) line. Phase separation of API 30˚ oil is shown in Figure 4C and particulates within the API 30˚ oil are visible in Figure 4D and

Figure 4E. It is possible that these crude oils may have a high portion of initial mass due to

particulates or heavy hydrocarbons (>C40) that are not soluble in n-hexane and are retained on

the filter paper, resulting in low recovery. Another possible explanation for the low recovery of

these API oils is that they may contain a high concentration of light hydrocarbons that are not

soluble in n-hexane and lost due to volatilization.

Table 4C. Recovery of API oils using EPA1664

Oil Density

(˚API) Prepared

Concentration

(mg/L)

Measured

Concentration

(mg/L)

Recovery

(%)

Standard

Deviation

(%)

40 25 16.5 65.82 13.36

40 50 30.5 61.19 0.99

30 24.7 15 60.66 3.78

30 50.5 32.1 63.52 4.83

20 25.8 20.1 77.63 4.59

20 52 44.6 85.96 4.57

Table 4D. Recovery of 25 mg/L synthetic produced water API 40˚ oil using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 23.8 12.7 53.47 25 13.4

2 23.8 19.0 80.00 25 20.0

3 23.8 15.2 64.00 25 16.0

Average 23.8 15.6 65.82 25 16.5

Std Dev 13.36 3.34

18

Table 4E. Recovery of 25 mg/L synthetic produced water API 30˚ oil using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 23.2 13.2 56.90 24.4 13.9

2 23.1 14 60.61 24.3 14.7

3 24.2 15.6 64.46 25.5 16.4

Average 23.5 14.3 60.66 24.7 15.0

Std Dev 3.78 1.29

Table 4F. Recovery of 25 mg/L synthetic produced water API 20˚ oil using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 24.5 17.9 73.06 25.8 18.8

2 25.9 21.3 82.24 27.3 22.4

3 23.2 18.0 77.59 24.4 18.9

Average 24.5 19.1 77.63 25.8 20.1

Std Dev 4.59 2.04

Table 4G. Recovery of 50 mg/L synthetic produced water API 40˚ oil using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 47.5 28.9 60.84 50 30.4

2 47.5 29.6 62.32 50 31.2

3 47.5 28.4 60.42 50 29.9

Average 47.5 29.0 61.19 50 30.5

Std Dev 0.99 0.63

Table 4H. Recovery of 50 mg/L synthetic produced water API 30˚ oil using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 47.8 32.0 66.95 50.3 33.7

2 48.0 31.5 65.63 50.5 33.2

3 48.1 27.9 58.00 50.6 29.4

Average 48.0 30.5 63.52 50.5 32.1

Std Dev 4.83 2.35

Table 4I. Recovery of 50 mg/L synthetic produced water API 20˚ oil using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 50.6 41.0 81.03 53.3 43.2

2 48.3 43.5 90.06 50.8 45.8

3 49.2 42.7 86.79 51.8 44.9

Average 49.4 42.4 85.96 52.0 44.6

Std Dev 4.57 1.34

19

Figure 4B. API oil recovery for 25 and 50 mg/L samples

Figure 4C. API 30˚ oil in separatory funnels after extraction

10

15

20

25

30

35

40

45

50

55

20 25 30 35 40 45 50 55

Oil

Rec

ov

ered

(m

g/L

)

Oil Added to Sample (mg/L)

API 20 API 30 API 40 Linear (1-to-1 line)

20

Figure 4D. API 30˚ oil particulates in organic phase

Figure 4E. API 30˚ oil residue on NaSO4 and filter paper

21

Effect of Environmental Factors

Salinity, temperature and pH Effects in Confocal analyses

Investigations showed that salinity affects the standard deviation of confocal analysis (Table 5A

and Table 5A-1), which generally indicates the precision of measurement. The higher salinity

showed larger standard deviation between measurements. As shown in equation (1), salinity will

have a significant influence on the mass transfer rate and evaporation rate. In the case of salinity,

higher salinity could cause faster movement of oil droplets, which in return would lead to larger

standard deviation measurements.

Salinity has pronounced effects on the interfacial tension in the crude oil/water system. At

different salinity concentrations, the surface tension will vary. It has been reported that the

presence of salt can alter the distribution of the surface active material present in oil phase to

aqueous phase due to salting-out effect [7]. Thus, salts can accelerate the diffusion of surface-

active components from bulk solutions to the interface. The measured interfacial tension values

can be correlated linearly to pressure (P), temperature (T), salt concentration (CB) in the aqueous

phase and surfactant concentration (Cs):

� � = �� + �� + � � + � � + � � (4)

The third effect of salinity on the oil/water system will be the change of electrical and surface

properties by the presence of Cl- ion in solution. Polarity plays an important role in increasing the

adherence between oil droplets, which depends on attractive forces [8]. Therefore, the cohesion

property, which refers to the attraction of a material, will facilitate the oil coalescence and

ultimately will influence the CLFM results.

Table 5A. Confocal analysis results for different salinities

25 ppm 50 ppm

Salinity

(mg/L)

Measured

Concentration

(mg/L)

Std

Dev

%

Recovery

%

stdv

Measured

Concentration

(mg/L)

Std

Dev

%

Recovery

%

stdv

0 22.31 2.03 89.24 8.13 42.54 6.27 85.08 12.54

35000 23.61 3.63 94.44 14.52 54.22 11.51 108.44 23.2

100000 24.10 5.94 96.4 23.76 52.08 9.02 104.16 18.04

250000 23.32 9.32 93.2 37.28 40.83 14.90 81.66 29.8

Table 5A-1. Confocal analysis data for different stacks and salinities

Salinity

(mg/L)

0 35000 100000 250000 0 35000 100000 250000

Stack 1 18.84 18.74 23.91 11 33.53 39.34 44.64 29.54

Stack 2 20.48 19.52 27.64 15.23 38.96 39.52 44.53 30.81

Stack 3 21.74 24.16 30.94 17.21 45.58 40.88 64.73 33.07

Stack 4 21.74 25.46 22.91 20.93 45.22 52.26 54.43 40.87

Stack 5 22.56 26.76 15.13 19.81 49.9 59.65 56.31 42.23

Stack 6 22.83 27.07 24.67 25.15 46.12 61.42 60.56 56.09

Stack 7 23.01 23.5 28.85 26.46 37.02 62.51 46.55 69.13

Stack 8 23.38 28.15 14.24 33.22 50.5 64.09 59.15 45.70

Stack 9 26.19 19.15 28.68 40.94 36.03 68.34 37.85 20.10

Higher standard deviations in the CLFM measurements were observed for pH values outside the

range of 3 – 7 (Table 5B and Table 5B-1) for crude oil concentrations of 25 ppm. The reason is

22

because crude oils contain amphiphilic molecules, such as asphaltenes and naphthenic acids that

can be found in large amounts in oils of high density. These chemical species can be ionized at

low and high pH values, since they are composed of acidic and basic functions with different

pKa. It has been shown that at low pH, the basic functions of oil species become positively

charged and these cationic species become less surface active. Therefore, the surface tension

should not have changed much at pH<7. However, at higher pH values (e.g. pH 8), acidic

asphaltenes are strongly interfacially active, which will cause a sharp decrease in interfacial

tension [9]. The theory behind this phenomenon is the reaction between alkali and crude oil. It

has been shown that alkali reacts with the acidic groups of the crude oil, generating interfacial

species called in situ surfactants. Those compounds could accumulate at the oil/water interface

and then reduce the interfacial tension. Therefore, at high pH values, the decreased surface

tension could have led to more variable CLFM results (i.e. higher standard deviations).

Interestingly, there was no clear trend observed for 50 ppm of crude oil. This could be due the

concentration of interfacial tension of active substances. For example, higher concentrations of

crude oil might contain more acidic groups in the mixture. In order to decrease the surface

tension, more alkali compounds are needed to complete the reaction. Therefore, higher pH may

be required to decrease the surface tension for 50 ppm of crude oil. In other words, at pH=8,

similar standard deviation are observed compared to lower pH values because of the stable

surface tension. However, further exploration might be required in the future to gain a better

understanding of this phenomenon.

Table 5B. Confocal analysis results for different pH

25 ppm 50 ppm

p

H

Measured

Concentratio

n (mg/L)

Standard

Deviatio

n

%

recover

y

%

stdv

Measured

Concentratio

n (mg/L)

Standard

Deviatio

n

%

Recover

y

%

stdv

3 24.26 4.44 97.04 17.7

6

51.86 8.6 103 17.2

1

4 25.52 5.51 102.08 22.0

4

57.9 13.46 115 26.9

3

5 27.01 5.45 108.04 21.8 51.61 13.2 103 2.40

6 27.70 4.39 1108 17.5

6

51.82 12.52 103 25.0

5

7 22.31 2.03 89.24 8.12 42.5 6.27 85.14 12.5

4

8 23.39 10.29 93.56 41.1

6

48.52 10.35 97 20.7

1

Table 5B-1. Confocal analysis data for different stacks and different pH

pH 3 4 5 6 8 3 4 5 6 8

Stack

1

19.89 19.95 19.48 22.41 14.02 43.25 36.35 35.19 35.19 31.58

Stack

2

22.27 25.65 21.25 24.6 15.82 45.13 46.71 35.63 37.38 37.68

Stack

3

23.3 30.97 26.43 27.57 30.09 45.27 46.96 39.32 42.38 43.28

Stack

4

31.59 21.86 26.71 30.73 35.66 49.26 48.93 47.1 45.13 48.54

Stack

5

27.02 33.37 28.23 33.52 13.94 50.18 62.9 49.9 49.4 49.12

Stack

6

26.43 18.16 32.28 31.52 30.37 50.82 63.62 61.04 61.56 49.35

23

Stack

7

28.89 28.83 34.69 21.68 35.1 63.12 68.47 61.98 62.9 51.43

Stack

8

19.99 20.87 32.47 25.39 8.57 68.89 73.4 64.5 65.45 61.18

Stack

9

19.00 30.10 21.60 31.88 26.96 50.86 73.76 69.85 66.91 64.53

At room temperature and lower temperatures, the results showed that the temperature did not

affect the confocal analysis accuracy significantly (Table 5C and Table 5C-1). However, the

standard deviation increased at higher temperatures. This could also be linked to the surface

tension change as indicated in equation (4). In addition, the intermolecular interactions can also

explain the trend in changing surface tension. More specifically, as the temperature of the system

increases, the Vander Waal forces holding the liquids together begin to weaken and decrease [10].

Therefore, the interfacial tension also decreases as the forces of attraction become reduced.

Temperature can also play an important role in interfacial interactions related to the dispersion

of oil by chemical dispersants [11]. As discussed above, the in situ surfactants can be generated

during reactions between alkali and crude oil. Thus, the temperature could also affect surface

tension by influencing (1) the kinetics of surfactant packing at the oil/water interface, (2)

diffusion of the surfactant through the oil slick, and (3) solubilization differences between the

polar and nonpolar ends of the surfactant molecule.

Furthermore, the increase in temperature will decrease the viscosity of liquids, which will lead to

increase velocity of separation according to stokes’ equation. It has been widely reported that at

high temperatures, the collision between particles due to the free bonds will increase [12]. As a

result the droplets will move faster and collide more frequently. This also confirms the theory that

an increasing of mass transfer coefficients, according to an increasing of temperature, will lead to

an increasing of the rate of mass transfer. All of these events attribute to an increased standard

deviation of the CLFM measurements.

Table 5C. Confocal analysis results for different temperatures

25 ppm 50 ppm

Temp

(oC)

Measured

Concentration

(mg/L)

Std Dev %

Recovery

%

stdv

Measured

Concentration

(mg/L)

Std Dev %

Recovery

%

stdv

4 22.10 3.16 88.4 8.6 40.77 8.9 81.9 17.8

20 22.31 2.03 89.2 8.1 42.56 6.27 85.1 12.5

60 27.04 5.50 108.1 11 49.64 11.41 99.2 22.8

Table 5C-1. Confocal analysis data for different stacks and different temperatures

Temperature

(oC)

4 60 4 60

Stack 1 17.87 19.03 30.73 33.41

Stack 2 18.61 21.61 33.55 44.19

Stack 3 21.78 25.41 38.84 51.76

Stack 4 23.99 27.08 44.72 55.39

Stack 5 24.57 30.69 56.03 63.45

Stack 6 26.02 30.78 30.89 42.21

Stack 7 19.33 36.28 47.13 68.21

Stack 8 25.96 30.24 49.06 41.85

Stack 9 20.81 22.30 35.98 46.30

24

EPA1664 – salinity effects It was observed that recovery and standard deviation was improved when using EPA1664 with

saline samples. Sodium chloride was added to Millipore water at three concentrations to represent

sea water (35,000) and typical salt content found in high salinity brine (produced water). Oil and

grease was recovered at 91-95%, 83-84%, and 92-96% for 35,000, 100,000, and 250,000 mg/L

saline samples, respectively, as shown in Table 5D. Results for triplicate 25 mg/L samples for

35,000, 100,000, and 250,000 mg/L salinities are shown in Table 5E, Table 5F, and Table 5G,

respectively. Results for triplicate 50 mg/L samples for 35,000, 100,000, and 250,000 mg/L

salinities are shown in Table 5H, Table 5I, and Table 5J, respectively. Recoveries are shown in

Figure 4F. It has been studied in colloid science that salt exists in the continuous phase (water)

and affects the electrostatic repulsion and equilibrium distance between droplets, thereby

stabilizing emulsions. Despite this theory, emulsion-breaking techniques were not required in

these experiments and the EPA 1664 method was not negatively affected by high salinity.

Table 5D. Recovery for synthetic produced water with crude oil at various salinities using EPA1664

Salinity

Concentration

(mg/L)

Prepared

Concentration

(mg/L)

Measured

Concentration

(mg/L)

Recovery

(%)

Standard

Deviation

(%)

35,000 25 23.8 95.21 2.39

35,000 50.6 46 91.53 6.25

100,000 25.1 21.2 84.44 4.12

100,000 49.3 41.3 83.81 0.96

250,000 25.8 23.8 92.04 5.27

250,000 48.4 46.5 96.20 1.85

Table 5E. Recovery for 25 mg/L synthetic produced water with crude oil at 35,000 mg/L salinity

using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 24.3 22.7 93.42 25.6 23.9

2 22.8 21.5 94.30 24.0 22.6

3 24.1 23.6 97.93 25.4 24.8

Average 23.7 22.6 95.21 25.0 23.8

Std Dev 0.02 1.11

Table 5F. Recovery for 25 mg/L synthetic produced water with crude oil at 100,000 mg/L salinity

using EPA 1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 23.5 18.8 80.00% 24.7 19.8

2 23.6 20.8 88.14% 24.8 21.9

3 24.3 20.7 85.19% 25.6 21.8

Average 23.8 20.1 84.44% 25.1 21.2

Std Dev 4.12 1.19

Table 5G. Recovery for 25 mg/L synthetic produced water with crude oil at 250,000 mg/L salinity

using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 25.4 22.3 87.80 26.7 23.5

25

2 23.9 21.6 90.38 25.2 22.7

3 24.3 23.8 97.94 25.6 25.1

Average 24.5 22.6 92.04 25.8 23.8

Std Dev 5.27 1.18

Table 5H. Recovery for 50 mg/L synthetic produced water with crude oil at 35,000 mg/L salinity

using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 45.4 39.9 89.26 47.8 42.0

2 49.0 42.5 86.73 51.6 44.7

3 49.9 48.6 98.60 52.5 51.2

Average 48.1 43.7 91.53 50.6 46.0

Std Dev 6.25 4.70

Table 5I. Recovery for 50 mg/L synthetic produced water with crude oil at 100,000 mg/L salinity

using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 45.4 39.9 89.26 47.8 42.0

2 49.0 42.5 86.73 51.6 44.7

3 49.9 48.6 98.60 52.5 51.2

Average 48.1 43.7 91.53 50.6 46.0

Std Dev 6.25 1.85

Table 5J. Recovery for 50 mg/L synthetic produced water with crude oil at 250,000 mg/L salinity

using EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 47.9 45.1 94.15 50.4 47.5

2 45.3 43.8 96.69 47.7 46.1

3 44.7 43.7 97.76 47.1 46.0

Average 46.0 44.2 96.20 48.4 46.5

Std Dev 1.85 0.82

26

Figure 4F. Recovery for synthetic produced water prepared with crude oil with varying salinity Confocal measurements in the presence of particles Silica (SiO2) particles are hydrophilic particles and have a strong negative charge.

Adding particles into the produced water system resulted in an effect on the confocal analysis

(Table 6A and Table 6A-1). The solution before and after adding the silica particles are showed

below in Figure 5A.

As shown in Table 6A, at 25 mg/L, very little effect was observed on the presence of silica

particles due to the small change on standard deviation for CLFM measurement. It has been

reported that solid colloidal particles constitute an important class of emulsifying agents, and are

commonly the cause of the high stability of water-in-crude oil emulsions. The emulsion stabilized

by inorganic particles is called Pickering emulsion [13]. In pickering emulsion, solid particles,

like silica, were adsorbed at the oil-water interface and impede the coalescence when two droplets

approach each other. Therefore, at 25 ppm, standard deviation for CLFM measurement didn’t change greatly.

However, at higher concentration (at 50 mg/L) for crude oil-water emulsion, bigger standard

deviation was observed for 50 ppm of silica. It is possible that at higher concentrations of

particles, there are more interactions between the particles and oil droplets. Those interactions

could interrupt the adsorption process and then break the emulsion. More than that, the

effectiveness of the finely divided solid in stabilizing oil water emulsions depends on factors like

particle size and shape, inter-particle interactions and the wettability of the particles by both

liquid phases [14]. Therefore, those parameters at different concentration of particles could

20

25

30

35

40

45

50

55

20 25 30 35 40 45 50 55

Oil

an

d G

rea

se R

eco

ve

red

(m

g/L

)

Oil and Grease Added to Sample (mg/L)

EPA 1664: 0 mg/L NaCl CLFM: 0 mg/L NaCl

EPA 1664: 35,000 mg/L NaCl CLFM: 35,000 mg/L NaCl



27

change and lead to an unstable emulsion.

Overall, the silica might have a positive effect on the stability of oil/water emulsion at certain

concentrations. However, this emulsion stability might relate to the concentration of particles, and

also the electrical and physical property of the particles, Therefore, in order to have a thorough

understanding of the stability for oil/water/particle emulsion, these particle parameters need to be

further evaluated.

Table 6A. Confocal analysis results for particles

25 mg/L 50 mg/L

Particle

Conc.

(mg/L)

Measured

Conc.

(mg/L)

Std

Dev

(mg/L)

%

Recovery

%

Std

Dev

Measured

Conc.

(mg/L)

Std Dev

(mg/L)

%

Recovery

%

Std

Dev

25 25.43 6.72 101.7 26 41.82 9.21 83.64 18

50 26.31 7.29 105.24 29 57.78 13.62 115.56 27

Table 6A-1. Confocal analysis data for different stacks and different particle concentrations

Particle Concentration

(mg/L)

25 50 25 50

Stack 1 17.16 15.48 30.69 39.82

Stack 2 17.9 17.6 39.07 42.32

Stack 3 20.97 20.52 35.46 54.61

Stack 4 28.19 23.97 46.82 59

Stack 5 30.12 27.83 44.61 66.41

Stack 6 33.42 29.23 58.72 70.49

Stack 7 33.64 33.37 37.39 78.78

Stack 8 28.81 33.95 32.63 44.06

Stack 9 18.73 34.91 50.99 64.53

Figure 5A. Oil water solution with silica added (a): before mixing, (b) after mixing

28

EPA1664 – Effect of Particles Similar to salinity, the recovery and standard deviation was improved when using EPA 1664

method with added solids (silicon dioxide SiO2, 10 and 50 mg/L). Recovery was approximately

92% when both concentrations of particles were added to synthetic produced water samples made

with crude oil as shown in Table 6B. Results for triplicate 25 mg/L samples for 10 and 50 mg/L

SiO2 are shown in Table 6C and Table 6D, respectively. Results for triplicate 50 mg/L samples

for 10 and 50 mg/L SiO2 are shown in Table 6E and Table 6F, respectively. It was observed that

the particles were coated by the crude oil when in the water phase (Figure 5B), but the oil coated

particles stabilized at the oil-water interface after extraction with n-hexane. It was observed that

the oil was washed off the particles by the n-hexane traveling through the sodium sulfate filter

during the drying process, and it can be concluded that the presence of particles did not have an

effect on the EPA 1664 method.

Table 6B. Recovery for synthetic produced water with crude oil and added particles using EPA1664

Particle

Concentration

(mg/L)

Prepared

Concentration

(mg/L)

Measured

Concentration

(mg/L)

Recovery

(%)

Standard

Deviation

(%)

10 27.1 25.1 92.57 4.26

10 48.9 45.1 92 3.08

50 26.9 23.7 88.11 3.9

50 48.8 44.6 91.5 1.6

Table 6C. Recovery for 25 mg/L synthetic produced water with 10 mg/L SiO2 particles using

EPA1664 Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration In

(mg/L)

Measured

Concentration

(mg/L)

1 25.7 22.9 89.11 27.1 24.1

2 26.2 25.5 97.33 27.6 26.8

3 25.2 23.0 91.27 26.5 24.2

Average 25.7 23.8 92.57 27.1 25.1

Std Dev 4.26 1.55

Table 6D. Recovery for 25 mg/L synthetic produced water with 50 mg/L SiO2 particles using

EPA1664

Sample Mass In

(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration In

(mg/L)

Measured

Concentration

(mg/L)

1 23.0 19.6 85.22 24.2 20.6

2 26.8 23.2 86.57 28.2 24.4

3 26.8 24.8 92.54 28.2 26.1

Average 25.5 22.5 88.11 26.9 23.7

Std Dev 3.90 2.80

29

Table 6E. Recovery for 50 mg/L synthetic produced water with 10 mg/L SiO2 particles using


(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration In

(mg/L)

Measured

Concentration

(mg/L)

1 45.5 40.3 88.57 47.9 42.4

2 47.4 44.8 94.51 49.9 47.2

3 46.6 43.3 92.92 49.1 45.6

Average 46.5 42.8 92.00 48.9 45.1

Std Dev 3.08 2.41

Table 6F. Recovery for 50 mg/L synthetic produced water with 50 mg/L SiO2 particles using


(mg)

Mass

Recovered

(mg)

Percent

Recovery

(%)

Concentration

In

(mg/L)

Measured

Concentration

(mg/L)

1 42.8 39.9 93.22 45.1 42.0

2 47.3 42.6 90.06 49.8 44.8

3 49.0 44.7 91.22 51.6 47.1

Average 46.4 42.4 91.50 48.8 44.6

Std Dev 1.60 2.53

30

Figure 5B. Oil coated particles stabilized at the oil-water interface. Oil coated SiO particles (a) before

extraction, (b) after extraction, and (c) and (d) during phase separation

Summary and Conclusions

Overall, the results from Task 16 confirmed the viability for using confocal to determine the

concentration of oil in water in the 25–50 mg/L range. Confocal results showed a wider range of

variability in concentrations/recoveries than EPA1664 depending on the specific conditions of the

sample (type of oil, oil concentration, environmental factors, and particles), but overall it was of

sufficient accuracy to determine the concentrations of oil. It is important to point out that more

accurate determination of the oil concentration using confocal microscopy will require more

samples to be analyzed than with the EPA method.

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Coalescence of Crude Oil Drops in Synthetic Produced Water. Energy & Fuels, 2015.

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3. Hutin, A., J.-F. Argillier, and D. Langevin, Mass Transfer between Crude Oil and Water.

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(a) (b)

(c) (d)

31

5. Fingas, M.F. The evaporation of oil spills: development and implementation of new

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transportation. Oil & gas science and technology, 2004. 59(5): p. 511-521.

9. Ramakrishnan, T. and D. Wasan, A model for interfacial activity of acidic crude oil/caustic

systems for alkaline flooding. Society of Petroleum Engineers Journal, 1983. 23(04): p.

602-612.

10. Kehinde, O.A., The effect of pH on interfacial properties of heavy oil/brine and adsorption.

2012.

11. Chapman, H., et al., The use of chemical dispersants to combat oil spills at sea: A review of

practice and research needs in Europe. Marine Pollution Bulletin, 2007. 54(7): p. 827-838.

12. Israelachvili, J.N., Intermolecular and surface forces: revised third edition. 2011:

Academic press.

13. Zhou, H., et al., Styrene-in-Water Emulsions Stabilized Solely by SiO2 Nanoparticles with

Tunable Wettablity. Asian Journal of Chemistry, 2013. 25(14): p. 8001.

14. P. Binks, B. and S. O. Lumsdon, Stability of oil-in-water emulsions stabilised by silica

particles. Physical Chemistry Chemical Physics, 1999. 1(12): p. 3007-3016.



October 19, 2016 A-2

ATTACHMENT 2. BENCH-SCALE TEST REQUIREMENTS

RPSEA

Subsea Produced Water Discharge

Sensor Bench-scale Test

Requirements

Document No.: 12121.6301.03.02

Subsea Produced Water Sensor Development

RPSEA Contract No. 12121-6301-03

September 30, 2015

Principal Investigator:

Jianfeng Zhang


16223 Park Row Drive, Suite 175

Houston, TX 77084

Document No. 12121.6301.03.02

Subsea Produced Water Discharge Sensor Bench-scale Test Requirements

September 30, 2015 Page 2 of 37

LEGAL NOTICE

This report was prepared by Clearview Subsea LLC as an account of work sponsored by the

Research Partnership to Secure Energy for America, RPSEA. Neither RPSEA members of

RPSEA, the National Energy Technology Laboratory, the U.S. Department of Energy, nor any

person acting on behalf of any of the entities:

a. MAKES ANY WARRANTY OR REPRESENTATION, EXPRESS OR IMPLIED WITH

RESPECT TO ACCURACY, COMPLETENESS, OR USEFULNESS OF THE INFORMATION

CONTAINED IN THIS DOCUMENT, OR THAT THE USE OF ANY INFORMATION,

APPARATUS, METHOD, OR PROCESS DISCLOSED IN THIS DOCUMENT MAY NOT

INFRINGE PRIVATELY OWNED RIGHTS, OR

b. ASSUMES ANY LIABILITY WITH RESPECT TO THE USE OF, OR FOR ANY AND ALL

DAMAGES RESULTING FROM THE USE OF, ANY INFORMATION, APPARATUS,

METHOD, OR PROCESS DISCLOSED IN THIS DOCUMENT.

THIS IS AN INTERIM REPORT. THEREFORE, ANY DATA, CALCULATIONS, OR CONCLUSIONS

REPORTED HEREIN SHOULD BE TREATED AS PRELIMINARY.

REFERENCE TO TRADE NAMES OR SPECIFIC COMMERCIAL PRODUCTS, COMMODITIES, OR

SERVICES IN THIS REPORT DOES NOT REPRESENT OR CONSTIITUTE AND ENDORSEMENT,

RECOMMENDATION, OR FAVORING BY RPSEA OR ITS CONTRACTORS OF THE SPECIFIC

COMMERCIAL PRODUCT, COMMODITY, OR SERVICE.

Document No. 12121.6301.03.02



Abstract

Bench scale testing is an important part of the Phase 2 project. Successful testing will allow the project

team to make a detailed assessment on how the selected sensors perform against the reference

Method EPA 1664.

To conduct bench scale testing, test requirements have been developed with input from the WPG

members. This report details the test requirements developed. It covers:

Test facility (test loop, test spool piece, test fluids, test procedures, data acquisition)

Test matrices with detailed test conditions

Data gathering and analysis

HSE and Quality aspects

Potential issues and concerns

In developing the test requirements, international standard such as ISO 15839 that had been specifically

developed for testing and evaluating online water quality measurement sensors have been considered.

Also experiences and lessons learned from previously conducted relevant JIP testing have been

incorporated.

A draft version of this report was first issued in August 2015 to the WPG members. Comments were

subsequently received from WPG members. These comments have now been incorporated into this

final version of the test requirements.

Tests usi g eal p odu ed ate a d tests of se so s fouli g itigatio e ha is s u de a ele ated pressure are no longer considered as part of the bench scale test program. The general consensus

among the WPG members was that for the bench scale tests, synthetic produced water would meet the

e ui e e ts. Also tests of se so s fouli g itigatio e ha is s u de a ele ated p essu e a e ot thought to be a priority in the current stage of the technology development.

New tests have been added to the test requirements. These include:

Effect of chemicals test: adding polymer to the list (Section 3.1.7)

Fouling test 2: this is added although a second fouling mechanism (different from using a crude

oil) is still to be decided (Section 3.1.8)

Effect of oil droplet size: adding 5 µm to the list (Section 3.1.10)

Repeat of the Oil Only tests (Section 3.1.11)

Repeat of the Oil and Solids tests (Section 3.1.12)

Document No. 12121.6301.03.02



Signature

Name: Jianfeng Zhang

Principal Investigator, 12121-6301-03

Clearview Subsea, LLC

Signed: ____________________________

Date: ____________________________

September 30, 2015

Document No. 12121.6301.03.02



THIS PAGE INTENTIONALLY LEFT BLANK

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Sensor Bench-scale

Test Requirements

A Report for


Project No: CVS002 Report No: 2015/168 Date: September 2015

NEL

This report is issued as part of the contract under which the

work has been carried out for the client.

NOTES

1 This report may be published in full by the client unless it includes information

supplied in confidence by TUV SUD Ltd or any third party. Such information, if included

within the report, shall be identified as confidential by TUV SUD Ltd.

2a The prior written consent of TUV SUD Ltd shall be obtained by the client before

publication by them of any extract from, or abridgement of, this report.

2b The prior written consent of TUV SUD Ltd shall be obtained by the client before

publication:

Where such publication is made in connection with any public enquiry, legal proceedings

or arbitration.

Where such publication is made in connection with any company prospectus or similar

document.

Where the client has notice that TUV SUD Ltd is seeking or intends to seek patent or like

protection for any intellectual property produced in the course of rendering the services.

NEL

Report No: 2015/168 Page 8 of 35 September 2015 Project No: CVS002

NEL

East Kilbride

GLASGOW G75 0QF

UK

Tel: +44 (0)1355 220222

Fax +44(0)1355 272999


Sensor Bench-scale

Test Requirements

A Report for


For

Brian Millington

Director

Date: September 2015

Prepared by:

Approved by:

Zak Latif / Ming Yang John Morgan

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CONTENTS

Page

ABBREVIATION ........................................................................................................................................... 10

1 INTRODUCTION ............................................................................................................................. 11

2 TEST FACILITY, REFERENCE METHODS AND TEST PROCEDURES .................................................. 12

2.1 Test Loop Design ............................................................................................................................ 12

2.2 Test Fluids ...................................................................................................................................... 13

2.3 Reference Methods........................................................................................................................ 14

2.4 Test Spool and Sensor Arrangement ............................................................................................. 15

2.5 Test Procedures ............................................................................................................................. 16

2.6 Data Acquisition and Sampling Procedure ..................................................................................... 16

3 TEST MATRICES .............................................................................................................................. 17

3.1 Description of Test Conditions ....................................................................................................... 17

3.2 Other Tests ..................................................................................................................................... 27

3.3 Additional Tests.............................................................................................................................. 27

3.4 Overall Test Matrix ......................................................................................................................... 28

4 TEST DATA, RESULTS ANALYSIS AND REPORTING ........................................................................ 29

4.1 Test Data ........................................................................................................................................ 29

4.2 Results Analysis .............................................................................................................................. 29

4.3 Reporting........................................................................................................................................ 29

5 HSE AND QUALITY ......................................................................................................................... 29

5.1 HSE ................................................................................................................................................. 29

5.2 Quality Assurance .......................................................................................................................... 30

6 POTENTIAL ISSUES AND CONCERNS ............................................................................................. 30

6.1 Test Matrix / Conditions ................................................................................................................ 30

6.2 Reference Methods........................................................................................................................ 33

6.3 Instrument Specific Issues and Problems ...................................................................................... 33

6.4 Others Tests ................................................................................................................................... 34

Reference .................................................................................................................................................... 35

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ABBREVIATION

API American Petroleum Institute

CLFM Confocal Laser Fluorescence Microscopy

DAQ Data Acquisition System

EOR Enhanced Oil Recovery

EPA Environment Protection Agency

GOM Gulf of Mexico

GPM Gallon Per Minute

HSE Health, Safety and Environment

JIP Joint Industry Project

LIF Laser Induced Fluorescence

m/s meters per second

MSDS Material Safety Data Sheets

N/A Not Available

OIW Oil-in-Water

PMP Project Management Plan

QA/QC Quality Assurance / Quality Control

QMS Quality Management System

RPSEA Research Partnership to Secure Energy for America

WPG Working Project Group

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1 INTRODUCTION

Bench scale testing is an important part of the Phase 2 project. Successful testing will allow the project

team to make a detailed assessment on how the selected sensors perform against the reference Method

EPA 1664.

To conduct bench scale testing, test requirements need to be developed. This report details the test

requirements developed. It covers:

Test facility (test loop, test spool piece, test fluids, test procedures, data acquisition)

Test matrices with detailed test conditions

Data gathering and analysis

HSE and Quality aspects

Potential issues and concerns

In developing the test requirements, international standard such as ISO 15839 that had been specifically

developed for testing and evaluating online water quality measurement sensors has been considered.

Also experiences and lessons learned from previously conducted relevant JIP testing have been

incorporated.

A draft version of this report was issued in August 2015 to the WPG members whose comments and input

were sought, in particular on potential issues and concerns raised in Section 6. Some of the key concerns

and issues raised in the draft report include:

Testing with heavy crude oils (API 15o and 20o) e.g., potential difficulty testing heavy crude at low

temperatures, supply of the crude oils

Gas bubble size, e.g., how to control bubble sizes

Testing at low temperature, e.g., 0.5 oC

High salinity test (requiring to dissolve a large amount of salt)

Soft and hard scale formation without damaging the test instruments

Test of se so s fouli g itigatio e ha is under high pressure

The use of real produced water for testing, e.g., issues with settling, bacteria and degradation

Since the issuing of the draft report, and further communications by the project team to the WPG

e e s spe ifi ally o testi g se so s fouli g itigatio e ha is u der an elevated pressure and

the use of field produced water for bench scale testing, comments were received from WPG members.

These comments have now been incorporated into this final report.

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2 TEST FACILITY, REFERENCE METHODS AND TEST PROCEDURES

2.1 Test Loop Design

To bench-scale test OIW monitoring devices, a test facility is required which can simulate produced water

with various conditions. Two approaches, each with their advantages and limitations, are discussed.

2.1.1 Recirculation Based Loop

A recirculation based loop uses a small mixing vessel to store a buffer of test fluids. The mixing vessel

feeds pipe-work connected to the test section before returning back to the vessel. After each test

condition, the loop is drained with the test fluids discarded unless the next tests to be immediately

followed only involve an increase in the oil or solid concentration. In general, once a test is conducted,

the test loop is then drained, and cleaned using clean water at an elevated temperature to remove any oil

or solids. After each cleaning process, samples are taken and analysed to determine if the test loop is

sufficiently clean to begin the next test.

With a small holding tank and circulating the test fluid at a velocity, a steady state can be reached quickly.

To generate oily water with a certain oil droplet size, different mixing mechanisms may be utilised, these

include power mixing, pipeline velocity and a stirrer fitted for the mixing vessel. Control of the oil droplet

size is not considered as an issue.

It should be pointed out that some recirculation loops may involve the re-use of test fluids. In this case,

perfect separation is required to separate the oil and / or solids before re-use, otherwise, the quality of

the test fluids cannot be guaranteed.

The main benefit of a recirculation loop is its ability to create stable and repeatable test conditions. Once

a mixture has been created and a steady-state is reached, the test fluid will remain stable. Also, due to

ei g ope ated i a at h ope atio , o ly s all ua tities of test fluids a e e ui ed; esulti g i a s all amount of wastewater being generated.

With a recirculation based test loop, making a change to test fluid salinity and temperature will be much

easier compared to a once-throughput based test loop.

Advantages and disadvantages of a recirculation based test loop are listed in Table 1.

Table 1 – Recirculation Based Loop

Advantages Disadvantages

Much less oil needed

Much less waste water generated

Easy to change salinity

Real Produced Water can be easily used

and tested

Potential contamination of oil and solids

left in the loop

Challenge in accommodating the testing

of Digitrol sensor (potential change of

droplet sizes over an extended test

period)

2.1.2 Once-throughput Based Loop

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A once-throughput configuration is a simple way of generating simulated produced water. A continuous

stream of oil is required to be injected into a constant water supply. Oil and water is then mixed using a

mixing device such as a shear valve before the test section. Once the oily water passes through the test

section, it is then taken away and disposed of.

With a high flow rate anticipated, making a change to the salinity of the test fluid cannot be easily done

for a once-throughput loop as a large amount of salt will need to be added to incoming water on a

continuous basis. There is also an issue regarding the use of real produced water for testing. At a rate of

50 m3/hr (3 m/s in a 3 inch pipe) in a once throughput loop, a large amount of real produced water is

required, even for a single test.

With this approach, a much larger quantity of test fluids are required due to its continuous nature. This

will lead to the need of disposing of a large amount of wastewater.

This approach, however, allows for fast concentration changes and does not require additional cleaning

time. However, the purity and supply of water used needs to be assured for the duration of each test.

Advantages and disadvantages of a once-throughput based test loop are given in Table 2.

Table 2 – Once-throughput Based Loop

Advantages Disadvantages

More consistent test conditions over

extended test duration

Easy to test the Digitrol sensor

Easy to change oil concentrations

Easy to clean the loop

Require a significant amount of oil

Generate a lot of waste water

Difficult to change salinity

Not so easy to change temperature

Large amounts of real Produced Water

required if this is used Notes: pipe at /s, flo ate: 50 m3/hr. At 100 mg/l, oil usage: 5 kg /hr.

2.2 Test Fluids

A variety of test conditions will be used in the testing program. Table 3 lists some of the key components

that will make up the test fluids. For an individual test, the exact mix will depend upon the test condition

and specification.

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Table 3 – Test Fluid Specification

Test Fluid Compositions Specification

Water Fresh water and saline water with at least three different

salinities.

Oil

Four different types of crude oil are required. Tests with

different oil droplet sizes will be included.

~15 API

~20 API

~30 API

~40 API

Solid

The type of solids used should be silica sand. Two sizes are

required:

~5 microns

~10 microns

Chemical

Four chemicals are required for the chemical tests. These

chemicals are typically used in offshore/oil and gas

production operations.

Corrosion Inhibitor

Deoiler

Hydrate Inhibitor

Polymer

Gas Air and Nitrogen are acceptable.

2.3 Reference Methods

Reference methods are required to provide reference values during the test program. The reference

values will be used as the basis with which the performance of each test instrument will be determined.

Three reference methods will be needed. These are respectively for the measurement of oil

concentration, solid concentration, and particle size of the test fluids. The reference methods must be

established properly with their uncertainties stated.

2.3.1 Oil Concentration

The most important reference method to be used in the test program is for the oil concentration

measurement. Here the EPA 1664 oil and grease measurement method will require to be used.

The test organization that will conduct the Phase 2 bench scale tests will have to demonstrate its

competence & compliance in developing and using the EPA 1664 Method.

As an alternative, the test organization may send oil and grease in water samples to a third party who

specialize in using the EPA 1664 Method.

It is critically important that the EPA 1664 Method is correctly developed and that oil and grease

measurement is properly conducted. The quality of the oil and grease reference data will partly

determine the success of the Phase 2 bench scale testing.

2.3.2 Solid Concentration

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Solids will be added i so e of the tests to dete i e its i flue e o test i st u e ts a ility to easu e oil and grease. Whilst only a limited number of tests are expected to involve solids, a reference method to

determine the solid concentration is required.

Solid concentration reference methods such as those listed below may be used:

Evaporation Method (Standard 2540 B)

Filtration Method (Standard 2540 D)

For a recirculation based test loop, reference solid concentration may also be estimated based on the

amount of solid added and the total volume of the test fluid in the loop. If such an estimate is to be used

as a reference, then it is important that the test organisation can demonstrate that no solids will settle

and accumulate in the test loop.

2.3.3 Particle Size Measurement

Similar to solid concentration, a particle/droplet size measurement reference method is required. The

main purpose of measuring the solid particle and oil droplet size is to ensure that the test fluid is

consistent during each of the tests and also between tests. However, measurement of solid particle size

and oil droplet size is less crucial than the oil and grease concentration measurement.

Solid particle and oil droplet size measurement should ideally be carried out by a non-testing instrument

from a different vendor to those being included in the test program. Potential candidate instruments are

listed below:

Malvern (light scattering)

Jorin (microscopy)

Coulter Counter (electrical)

2.4 Test Spool and Sensor Arrangement

The arrangement of the sensors and the design of the test spool piece are extremely important. They

must be done in a manner in which the test fluid remains consistent throughout the test spool.

Installation effects and flow disturbances are also addressed. In addition all the instruments can be tested

simultaneously.

It is recommended that the sensors are installed in a vertical test spool with test fluid travelling in an

upwards direction. This will provide a better chance to ensure the test fluid in test spool piece is

consistent throughout (i.e., well mixed across the pipe diameter). The sensors must be adequately spaced

a ay f o e ds o othe i st u e ts / o st u tio s, aki g su e that e do s i stallatio requirements are met. During commissioning it is imperative that trials are conducted to ensure test

fluid s o siste y alo g the test spool pie e.

Summary requirements for the test section:

Test section must be oriented vertically

Flow must be travelling upwards

A li d T pie e eeds to e incorporated at the start of the test section (instead of a bend)

Sensors must be spaced properly and i ge e al e do s i stallatio e ui e e ts should e et The consistency of the test fluid must not be affected by the installations of the individual sensors

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2.5 Test Procedures

The test organization is required to produce test procedures detailing how each of the tests will be

conducted.

2.6 Data Acquisition and Sampling Procedure

2.6.1 Data Acquisition

A data acquisition system (DAQ) must be set up to log the following test facility parameters for each test:

Pressure

Temperature

Flow rate

A system must also be in place to record the three reference values:

Oil Concentration (critically important)

Solid Concentration

Droplet / Particle Size

Corresponding data from each of the four test instruments must be recorded.

Vendors should be consulted as to how the raw data from their test instruments should be recorded and

how these raw data files should be processed to obtain the necessary results that can be compared to the

reference values.

2.6.1 Sampling and Sample Handling Procedures

Sampling will be required for both oil concentration and solid concentration reference measurements.

Inappropriate sampling set-up and methodology can introduce a significant amount of uncertainty to the

reference oil and solid concentration results.

To ensure proper sampling and sampling handling, the following points should be taken into

consideration:

Proper sample points using a centre line pitot

Sample probe facing upstream

Samples shall be taken isokinetically

Sample bottles need to be scrupulously clean and should be solvent washed and dried before use

If samples are not analysed immediately, acid should be added to prevent bacteria action

Strict sampling and sample handling procedures need to be established and followed

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3 TEST MATRICES

In order to properly assess the performance of the four sensors, two blocks of tests have been scheduled.

The first block of tests will make up the majority of the test program which will be undertaken. The time

allocated to complete these tests is estimated at about 45 days. The actual test time will depend on the

testing facility to be used.

The second block of tests, for which 15 days have been allocated, will be considered as additional tests.

The additional tests are to be established once the results from the first block of tests will have been

analysed and shared with the WPG members. They may include some repeating tests or completely new

tests.

Further discussion on additional tests is given in section 3.3.

3.1 Description of Test Conditions

The sections below describe the test conditions required to be undertaken. Tests may be grouped into

th ee atego ies; Baseli e, Effe t of a d Othe s.

Baseline tests are essentially Oil Only tests. Here tests with different types of oil and concentrations will

be conducted. These tests are very important as they form the basis with which effect of other

parameters can be determined.

The Effe t of tests a e desig ed to see the effe t of othe pa a ete s o the easu e e t of the test instruments. The parameters will include:

Solids

Gas bubbles

Chemicals

Temperature

Flow velocity

Salinity

Oil droplet size

After the Oil Only tests, the Oil and Solids tests will be carried out. These will determine if the instruments

are able to differentiate between the oil droplets and solid particles and test if the addition of solids

influences the oil concentration / droplet size measurement.

Effe t of tests highlighted i ed may not include solids (with the exception to gas bubbles and flow

velocity effect tests). Whether solids will be added in these tests will be determined following the Oil and

Solids tests, i.e., if the results from the Oil and Solids tests show that solids do have an effect, then solids

ay e i luded i the Effe t of tests (highlighted in red).

For all the tests, the default position will be:

Salinity: depends on test facility (fresh or seawater)

Flow velocity in the test section: 3 m/s

Temperature: ambient

Oil droplet size: 15 µm

API of crude oil: 30o

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3.1.1 Oil Only

The first test comprises of Oil Only. As described in the Test Fluid section, four crude oils with an API of

15, 20, 30, and 40 are to be tested.

The crude oils used in this test, and throughout the rest of the testing will be supplied to the testing

facility prior to the commencement of the test program. Details of the Oil Only tests are shown in Table 4

to Table 7.

Table 4 – 15o API Oil Only Test Condition

Nominal Oil Concentration

(mg/L)

Target Oil Droplet Size

(µm)

Oil Type

(API)

10 ~15 ~15 15 ~15 ~15 20 ~15 ~15 25 ~15 ~15 30 ~15 ~15 50 ~15 ~15

100 ~15 ~15 200 ~15 ~15



(mg/L)


(µm)

Oil Type

(API)

10 ~15 ~20 15 ~15 ~20 20 ~15 ~20 25 ~15 ~20 30 ~15 ~20 50 ~15 ~20

100 ~15 ~20 200 ~15 ~20



(mg/L)


(µm)

Oil Type

(API)

10 ~15 ~30 15 ~15 ~30 20 ~15 ~30 25 ~15 ~30 30 ~15 ~30 50 ~15 ~30

100 ~15 ~30

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200 ~15 ~30



(mg/L)


(µm)

Oil Type

(API)

10 ~15 ~40 15 ~15 ~40 20 ~15 ~40 25 ~15 ~40 30 ~15 ~40 50 ~15 ~40

100 ~15 ~40 200 ~15 ~40

3.1.2 Salinity

The pu pose of the Effe t of “ali ity test is to dete i e if a ha ge i sali ity ill i pa t the instruments ability to measure oil. This test is currently planned to be conducted without the presence of

solid particles.

The Effe t of “ali ity test involves creating three saline solutions and using them to prepare oily water to

test the four sensors under Oil Only conditions. Three salinity levels are required; 35,000, 100,000, and

250,000 mg/L, respectively.

Details of Salinity effect test conditions are shown in Table 8 to Table 10.

Table 8 – 35,000 mg/L Salinity Test Condition

Nominal Oil

Concentration

(mg/L)


(µm)

Oil Type

(API)

Salinity

(mg/L)

10 ~15 ~30 35,000

20 ~15 ~30 35,000

30 ~15 ~30 35,000

50 ~15 ~30 35,000

100 ~15 ~30 35,000

200 ~15 ~30 35,000


Nominal Oil

Concentration

(mg/L)


(µm)

Oil Type

(API)

Salinity

(mg/L)

10 ~15 ~30 100,000

20 ~15 ~30 100,000

30 ~15 ~30 100,000

50 ~15 ~30 100,000

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100 ~15 ~30 100,000

200 ~15 ~30 100,000


Nominal Oil

Concentration

(mg/L)


(µm)

Oil Type

(API)

Salinity

(mg/L)

10 ~15 ~30 250,000

20 ~15 ~30 250,000

30 ~15 ~30 250,000

50 ~15 ~30 250,000

100 ~15 ~30 250,000

200 ~15 ~30 250,000

3.1.3 Oil and Solids

Four Oil and Solid tests are required to be completed. The tests are carried out in a similar manner to the

Oil Only test; however in the presence of solids with two concentrations and two sizes.

The first two tests as detailed in Table 11 will be with a solid concentration of 10 mg/L, but at two

different particle sizes, 5 and 10 µm, respectively. The second two tests as shown in Table 12 will be a

repeat of the first two but at a higher solid concentration of 50 mg/L.

Results from the Oil and Solids tests will be used to determine whether solids should be included in some

of the othe Effe t of tests as dis ussed ea lie .

Table 11 – Oil and 10 mg/L Solids Test Condition

Nominal Oil

Concentration

(mg/L)

Nominal Solid

Concentration

(mg/L)

Solid Particle Size

(µm)


(µm)

10 10 ~5 ~15

20 10 ~5 ~15

30 10 ~5 ~15

50 10 ~5 ~15

100 10 ~5 ~15

200 10 ~5 ~15

10 10 ~10 ~15

20 10 ~10 ~15

30 10 ~10 ~15

50 10 ~10 ~15

100 10 ~10 ~15

200 10 ~10 ~15

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Table 12 – Oil and 50 mg/L Solids Test Condition

Nominal Oil

Concentration

(mg/L)

Nominal Solid

Concentration

(mg/L)

Solid Particle Size

(µm)


(µm)

10 50 ~5 ~15

20 50 ~5 ~15

30 50 ~5 ~15

50 50 ~5 ~15

100 50 ~5 ~15

200 50 ~5 ~15

10 50 ~10 ~15

20 50 ~10 ~15

30 50 ~10 ~15

50 50 ~10 ~15

100 50 ~10 ~15

200 50 ~10 ~15

3.1.4 Effect of Gas Bubbles

The pu pose of this test is to see if the p ese e of gas u les ould affe t se so s easu e e t capability.

Gas at a flow rate of 0.5% is to be injected with an oil concentration of 30 mg/L. It is also proposed that

two sizes of gas bubbles are to be used although this may be potentially difficult to achieve. In addition,

solids at a concentration of 50 mg/L are also added.

The exact gas bubble sizes have not been determined; they are simply stated as Size 1 and Size 2 at the

present time. The test organisation will be required to undertake work to determine the achievable size

of gas bubbles that can be formed.

Details of the Effect of Gas Bubbles test are given in Table 13.

Table 13 – Effect of Gas Bubbles Test Condition

Nominal Oil

Concentration

(mg/L)

Target Oil

Droplet Size

(µm)

Nominal Solid

Concentration

(mg/L)

Solid Particle

Size

(µm)

Gas Bubbles Gas Bubble

Size

(µm)

10 ~15 0 N/A N/A

10 ~15 0 N/A Size 1

10 ~15 0 N/A Size 2

10 ~15 10 ~5 N/A

10 ~15 10 ~5 Size 1

10 ~15 10 ~5 Size 2

30 ~15 0 N/A N/A

30 ~15 0 N/A Size 1

30 ~15 0 N/A Size 2

30 ~15 10 ~5 N/A

30 ~15 10 ~5 Size 1

30 ~15 10 ~5 Size 2

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Note: - No Gas, - Gas included

3.1.5 Effect of Flow Velocity

Three flow velocities are to be used to test the sensors to see if a change of the flow velocity would affect

i st u e ts pe fo a e. Details of the test a e gi e i Ta le .

Table 14 – Effect of Flow Variation Test Condition

Nominal Oil

Concentration

(mg/L)

Target Oil Droplet

Size

(µm)

Nominal Solid

Concentration

(mg/L)

Solid Particle Size

(µm)

Flow Velocity

(m/s) [ft/s]

30 ~15 0 N/A 1.5 [5]

30 ~15 0 N/A 3 [10]

30 ~15 0 N/A 4.5 [15]

30 ~15 10 ~5 1.5 [5]

30 ~15 10 ~5 3 [10]

30 ~15 10 ~5 4.5 [15]

3.1.6 Effect of Temperature Variation

The pu pose of the Effe t of Te pe atu e Va iatio is to dete i e if a ha ge of te pe atu e has a impact on the test instruments ability to measure oil concentration.

For this test, four temperature conditions as detailed in Table 15 are required to be undertaken: 0.5, 25,

65, and 90oC.

Table 15 – Effect of Temperature Variation Test Condition


(mg/L)


(µm)

Temperature

(oC) [oF]

30 ~15 0.5 [33]

30 ~15 25 [80]

30 ~15 65 [150]

30 ~15 90 [200]

3.1.7 Effect of Chemicals

At least four different types of chemicals are to be tested. The final concentration for each of the

chemicals may still change. Currently suggested values are given in Table 16.

Four types of chemicals proposed for testing include:

Corrosion Inhibitor

Deoiler

Hydrate Inhibitor

Polymer

The MSDS of the chemicals will be obtained and forwarded to the WPG members and testing facility prior

to the test program.

NEL


The pu pose of the Effe t of Che i als test is to dete i e the effe t that diffe e t he i als ay ha e on the performance of the instruments. The addition of chemicals may alter the oil droplet size. However,

as the aim of the test is to determine the effect that the chemicals may ha e o the i st u e t s measurements, the oil droplet size should be kept unchanged by the introduction of the chemicals. The

effe t of d oplet size o se so s pe fo a e ill e the su je t of a sepa ate test as detailed in Section

3.1.10.

It has been noted that some chemicals may influence the oil concentration reference method (EPA 1664).

This needs to be looked into in more detail prior to the commencement of the test program.

Table 16 – Effect of Chemicals Test Condition


(mg/L)

Chemical Concentration

(mg/L)

Chemical

type

10 10 Corrosion Inhibitor



10 10 Deoiler

30 10 Deoiler

50 10 Deoiler

10 50 Hydrate Inhibitor



10 100 Polymer

30 100 Polymer

50 100 Polymer

3.1.8 Fouling Test

The fouli g test is desig ed to dete i e the effe ti e ess of a i st u e t s lea i g e ha is . T o fouling tests are being suggested here, one involves manual fouling with crude oil, and another one for

which how the fouling will be induced is yet to be decided.

C ude oil fouli g of the i st u e ts ill e i du ed a ually i hi h the i st u e t s opti al i do s will be left covered by a crude oil for a minimum of 24 hours before a low oil concentration test is carried

out, e.g., 30 mg/L. Details of the test conditions are shown in Table 17.

However, it is understood that there are other forms of fouling which may be considered. These are

discussed further in Section 6.

NEL


Table 17 – Fouling Test Condition


(mg/L)


(µm)

Manual Fouling

30 ~15 Fouling by crude oil

30 ~15 Scale fouling to be decided

3.1.9 Memory Test

The purpose of this test is to determine if the instruments would suffer a memory effect. Each memory

test will consist of three parts:

Part 1 – Low Concentration

Part 2 – High Concentration

Part 3 – Low Concentration

Each test will start with a low concentration (30 mg/L), increasing to a high concentration (e.g., 500mg/)

for a period of time (e.g., 30 minutes or longer) before being reduced back to a low concentration

(30mg/L).

To cover the different scenarios that may be encountered in real life, three different high concentrations

have been suggested for testing. These include 500, 2000, and 5000 mg/l.

The cleaning schedule set by each of the test instrument suppliers will not be altered for the memory

test.

Details of the Memory Test are shown in Table 18 to Table 20.

Table 18 – Memory Test 1 Condition


(mg/L)


(µm)

30 ~15

500 ~30

30 ~15



(mg/L)


(µm)

30 ~15

2000 ~30

30 ~15



(mg/L)


(µm)

30 ~15

NEL


5000 ~50

30 ~15

3.1.10 Effect of Oil Droplet Size

The pu pose of this test is to see if a ha ge i oil d oplet size ould affe t the se so s pe fo a e.

Oil droplet will have a mean size (Dv50) of 15 µm for most of the tests in the test program. The size has

been chosen based on the performance of hydrocyclones for which it is commonly accepted that oil

droplets smaller than this will become difficult to be separated.

For deepwater subsea separation and produced water treatment applications, hydrocyclone would

represent a reasonable technology for simulation.

It is proposed to trial three other droplet sizes for the current test (Dv50): 5 µm, 10 µm and 20 µm. The

test with 15 µm droplet size has already been covered. Details of the test are given in Table 21.

Table 21 – Effect of Oil Droplet Size Test Condition


(mg/L)


(µm)

30 ~5

30 ~10

30 ~20

3.1.11 Oil Only Repeat

This is a repeat of the Oil Only test as detailed in 3.1.1. However the repeat tests will be conducted only

with two crude oils (20, 30) instead of four.

The purpose of the test is to see how the sensors perform under repeated test conditions. The reason to

repeat the Oil Only test here is because it is a baseline test.

Table 22 – 20o API Oil Only Repeat Test Condition


(mg/L)


(µm)

Oil Type

(API)

10 ~15 ~20 15 ~15 ~20 20 ~15 ~20 25 ~15 ~20 30 ~15 ~20 50 ~15 ~20

100 ~15 ~20 200 ~15 ~20

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Table 23 – 30o API Oil Only Repeat Test Condition


(mg/L)


(µm)

Oil Type

(API)

10 ~15 ~30 15 ~15 ~30 20 ~15 ~30 25 ~15 ~30 30 ~15 ~30 50 ~15 ~30

100 ~15 ~30 200 ~15 ~30

3.1.12 Oil and Solids Repeat

Like Oil Only Repeat test, the purpose of the test is to see how the sensors perform under repeated test

conditions. The reason to repeat Oil and Solids test is due to the fact that solids will be always present in

the produced water along with the oil droplets.

Test conditions will be identical to those given Section 3.1.3, which are shown in Table 24 and 25.

Table 21 – Oil and 10 mg/L Solids Repeat Test Condition

Nominal Oil

Concentration

(mg/L)

Nominal Solid

Concentration

(mg/L)

Solid Particle Size

(µm)


(µm)

10 10 ~5 ~15

20 10 ~5 ~15

30 10 ~5 ~15

50 10 ~5 ~15

100 10 ~5 ~15

200 10 ~5 ~15

10 10 ~10 ~15

20 10 ~10 ~15

30 10 ~10 ~15

50 10 ~10 ~15

100 10 ~10 ~15

200 10 ~10 ~15

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Table 22 – Oil and 50 mg/L Solids Repeat Test Condition

Nominal Oil

Concentration

(mg/L)

Nominal Solid

Concentration

(mg/L)

Solid Particle Size

(µm)


(µm)

10 50 ~5 ~15

20 50 ~5 ~15

30 50 ~5 ~15

50 50 ~5 ~15

100 50 ~5 ~15

200 50 ~5 ~15

10 50 ~10 ~15

20 50 ~10 ~15

30 50 ~10 ~15

50 50 ~10 ~15

100 50 ~10 ~15

200 50 ~10 ~15

3.2 Other Tests

3.2.1 Cleaning Mechanism Test

It as suggested that se so s lea i g e ha is s e e tested u de a high p essu e e.g., psi condition. However majority comments received from WPG members following the issuing of the test

requirements draft report indicated that this may not be considered as a priority at the present time. Also

it has become clear that ProAnalysis would not supply an automatic retractable unit that is designed to

o k at a ele ated p essu e fo the Phase e h s ale testi g. As a esult, test of se so s lea ing

mechanisms under an elevated pressure will not be included as part of the Phase 2 bench scale test.

3.2.2 Real Produced Water

Testing using real produced water has been discussed and debated. Again majority comments received

from WPG members following the issuing of the test requirements draft report indicated that synthetic /

simulated produced water should be preferred for the bench scale test. Using real produced water for lab

bench scale testing will present issues in terms of logistic, quality and representativeness of the produced

water. Also it has been pointed out that field tests (with real produced water) will be required anyway at

a later stage. As a result, only synthetic produced water will be used in the bench scale test.

3.3 Additional Tests

Additional tests are planned to be carried out once the results from the first block of tests will have been

analysed and shared with the WPG members. A total of 15 days have been allocated for additional tests.

Whilst the additional tests will only be established after the conduct of the first block of tests and the

review of the results obtained, initial thoughts on potential additional tests may include:

Repeating some of the tests conducted in the first block of tests

Tests with additional chemicals

Fouling mitigation tests with some soft / hard scale

NEL


Test fluids containing a full mix of oil, solid particles, chemicals, gas bubbles to mimic real

produced water

NEL


3.4 Overall Test Matrix

The proposed test matrix combines elements from the above described test conditions.

Table 23 – Proposed Overall Test Matrix

Test Types API

Oil Solids

Gas Size Chem.

(mg/l)

Temp.

(oC)

Manual

Induced

Fouling

Flow rate

(m/s)

Salinity

( ’s mg/l)

Est. Time

(Days) Conc

(mg/l)

Size (µm) Conc

(mg/l

)

Size

(µm)

Oil only 15, 20,

30, 40

10, 15, 20, 25,

30, 50, 100,

200

~15 5

Salinity 30 10, 20, 30,

50, 100, 200 ~15 35, 100, 250 5

Oil & Solids 30 10, 20, 30,

50, 100, 200 ~15 10, 50 5, 10 5

Effect of Gas bubbles 30 10, 30 ~15 0, 10 5 1 , 2 4

Effect of Chemical 1 30 10, 30, 50 ~15 10 1


Effect of Chemical 3 30 10, 30, 50 ~15 50? 1


Effect of Temperature 30 30 ~15 0.5, 25, 65, 90 2

Fouling Test 1 30 30 ~15 3

Fouling Test 2 30 30 ~15 4

Memory Test 1 30 30, 500, 30 ~15, 30, 15 1

Memory Test 2 30 30, 2000, 30 ~15, 50, 15 1

Memory Test 3 30 30, 5000, 30 ~15, 50, 15 2

Effect of Flow Velocity 30 30 ~15 0, 10 5 1.5, 3, 4.5 2

Effect of Droplet Size 30 30 10, 15 1

Oil Only Repeat 20, 30

10, 15, 20, 25,

30, 50, 100,

200

~15 2

Oil & Solids Repeat 30 10, 20, 30, ~15 10, 50 5, 10 5

NEL


50, 100, 200

NEL


4 TEST DATA, RESULTS ANALYSIS AND REPORTING

4.1 Test Data

Required test data shall include:

Reference values: oil and grease concentration, solids concentration, oil droplet size and solid

particle size

Data from test instruments: oil and grease concentration (all test instruments), solids

concentration, oil droplet and solid particle size if measured

Test conditions: flow rate, temperature and pressure

In addition to the test data, test details shall be logged and recorded in terms of time, type of tests, oil

and grease samples taken, test conditions, and any particular events that will have taken place, e.g.,

i st u e t alfu tio o e do s i te e tio o site a d / o ia e ote a ess .

4.2 Results Analysis

As a minimum, results analysis shall include:

Plotting results from test instruments along with reference data

Calculation of errors (i.e., differences between instrument readings and reference values) for

ea h of the test o ditio s. This eeds to e do e at least fo the oil a d g ease data

An assessment on instrument repeatability, response time and memory effect

4.3 Reporting

The test report will need to include the following sections as a minimum:

Description of the test facilities (test loop, test section, test fluids and test instrument

arrangement, and mechanisms in which how oil, solids, chemicals, and gas bubbles are injected

and produced water simulated)

Description of installation and commissioning of the test instruments (including calibrations)

Description of procedures for each of the tests

Description of the reference methods and expected uncertainties

Test results and discussion

Assessment on instrument performance and identification of areas for improvements

Conclusions and recommendations

5 HSE AND QUALITY

5.1 HSE

The test organisation will be required to show its current practice and record on HSE. As a minimum, the

test organisation needs to provide the following:

Current HSE practices in observing local regulations

Recent HSE records

The HSE activities must cover as a minimum the following areas:

NEL


Laboratory aspect (including general laboratory HSE, handling of synthetic produced water (PW),

crude oil, solvents / chemicals and sand particles, use of laser, waste, etc.)

Testing and facility aspect (including heavy good lifting, waste water, electrical, flooding plus

those mentioned in the laboratory aspect above)

The health and safety processes will be based on compliance with the terms of the Occupational Safety

and Health Act 1970, subsequent legislation, and will provide and maintain a healthy and safe working

environment. The health and safety objective of the Company is to minimize the number of instances of

occupational accidents and illnesses and ultimately achieve an accident-free workplace.

On the environmental front, the test organization must take notice and demonstrate the following:

Prevent oil and other pollution

Minimize as far as possible any adverse impact on the environment from its activities,

Continually improve its performance against targets associated with its significant environmental

aspects, and

Fully comply with all applicable environmental legislation

5.2 Quality Assurance

The test organization will be required to show their QA/QC processes. The following aspects shall be

observed [1]:

Perform the work in accordance with International Standards for Project Quality Assurance and

key principles of ISO 9001:2008

Require quality in all project work activities and deliverables through leadership, individual

commitment and the implementation of the Quality Management System (QMS) through

documented processes and procedures

Support an internal and external audit / review program to provide assurance of compliance with

the quality requirements during all stages of the project, and

Continually improve its effectiveness through a regular cycle of measurement and review

6 POTENTIAL ISSUES AND CONCERNS

The project plan is to carry out as many tests as possible within an allocated project budget and time

scale. Also test fluids should mimic real produced water as closely as possible. However, there are

potential issues and concerns, these are discussed here.

Following the issuing of the draft test requirements report, comments and input have been received from

WPG members. As a result most of the key issues and concerns raised early are now considered to be

resolved. The discussion below reflects the comments and inputs received to date.

6.1 Test Matrix / Conditions

6.1.1 Oil Only

It was agreed by the WPG members at the 6th WPG meeting that 4 types of oils with API respectively of

15o, 20o, 30o, 40o should be tested.

NEL


Potential issues may include:

Supply of oils (who / where / logistics / MSDS). The required quantity of these oils will also

depend on test facilities. A much smaller quantity will be required if the tests are to be conducted

using a recirculation based loop

Testing of heavy crude at low temperature will be problematic

Fo the Effe t of tests, it is suggested that a ude oil ith a API of a ou d o is used.

6.1.2 Effect of Gas Bubbles

For surface produced water discharge applications, due to a pressure reduction at treatment systems

such as hydrocyclones, or degassers, produced water may contain a fair amount of gas bubbles. The

presence of gas bubbles may also result from the use of a gas flotation unit, which is common in the GoM

for treating produced water before overboard discharge.

Inputs are sought from WPG members on the following questions:

Any suggestion on how gas bubbles may be created?

It looks that gas bubbles will be induced in the bench scale test. Thus it is likely that the size of the gas

u les ill e i the s i o s athe tha s of i o s.

6.1.3 Effect of Temperature

Currently four temperatures, 0.5, 25, 65, and 90 oC, have been suggested in the test program.

Potential challenges are likely to be encountered in particular for the 0.5 oC test. Perhaps this will

be more difficult to achieve for a once-throughput test loop because of the large amount of test

fluids involved.

6.1.4 Effect of Chemicals

In the draft test requirements report, tests with three different types of chemicals were suggested.

Proposed types of chemicals include: corrosion inhibitors, deoilers and hydrate inhibitors. The purpose of

the tests is to see how chemicals may affect the performance of the sensors.

Comments from WPG members on the following questions were sought:

Do you agree that we have chosen the right types of production chemicals for testing?

If not, what would you suggest and prioritise?

As a result, a fourth chemical, a polymer, has been added. Polymers are increasingly used as part of

chemical EOR operations. These polymers will eventually show in the produced water.

MSDS and typical dosing information can be obtained from vendors. It is also believed that supply of

these chemicals should not pose a big issue. Once again, however, the quantity required for testing will

depend upon which type of test loop will be used.

NEL


One of the issues brought up at the recent WPG meeting was that chemicals might also impact on the

measurement of the reference oil and grease. This, however, should not be considered as a concern.

Oil and grease is defined by the EPA Method. Therefore, if the presence of a production chemical in the

produced water does have an impact, it is the result from the EPA Method that is considered as the

t ue alue al eit this t ue alue ay e diffe ent from those analyses of samples taken from tests

under the same oil and grease concentration but without the presence of the production chemical). Oil

a d g ease easu ed y the test i st u e ts ill eed to e o pa ed to the affe ted t ue alue fro the EPA ethod. The se so eadi gs ay ha e la ge e o due to the he i al dete tio y the sensor but not extractable by EPA Method 1664, or extracted by EPA Method 1664 but incapable of being

detected by the sensor.

6.1.5 Effect of Salinity

Up to 250,000 mg/l has been suggested for being included in the test. Assuming sodium chloride is used

for the salinity test, 250 kg of sodium chloride will require to be added for every cubic meter of water.

The process of dissolving the salt will be time consuming.

Testing with a once throughput at such a high salinity will be prohibitively expensive and may not be

practically possible due to the large amount of test fluid required. Plan should be made to reduce the

salinity or duration without major impact on achieving the goal of testing with salinity.

6.1.6 Fouling Test

Fouling mitigation has been identified as one of the most important parameters when it comes to

se so s elia ility a d pe fo a e. This as lea ly efle ted i the gap a alysis of Phase I of the project

in which a weighting factor of 20% was given compared to the 4% assigned to most other parameters.

Therefore, it is important that fouling mitigation testing is carried out.

The suggested approach in the draft test requirements report was to coat the sensors with a crude oil and

then re-test the instruments to see if they can clean the fouling and measure the oil and grease correctly.

A alte ati e app oa h is to ush so e oil a d g ease di e tly o the se so s opti al i do s, which

is considered to be more difficult to clean than crude oil.

Fouling may result from:

Oil coating

Soft scale formation

Deposition of bio-sludge linked to bacteria action

Hard scale formation

Asphaltenes

Comments from WPG members on the following questions were sought:

What additional fouling tests would you like to see being conducted?

How to recreate representative soft scale and / or hard scale in a laboratory environment for the

sensors without permanently damaging them?

Hard scale and asphaltenes may damage the sensor, and they will likely require solvent soaking during

subsea operation of the sensors. So tests should not be performed in this project.

NEL


Ha i g o side ed the i po ta e of the se so s fouli g itigatio e ha is , a se ond fouling test has

now been suggested and added to the test matrix. How the fouling will be induced in this second fouling

test is yet to be determined.

6.2 Reference Methods

Whilst reference values will need to be provided on parameters including oil and grease concentration,

solid concentration, oil droplet size and solid particle size as well as temperature, pressure and flow rate

of the test fluid, oil and grease reference will undoubtedly be most important. The quality of the oil and

grease data will impact significantly on the outcome of the Phase 2 testing.

Challenges

Both shortlisted test organisations for the Phase 2 testing are based in UK where the EPA 1664 Method is

not commonly used. Each will need to develop the method if the samples are to be analysed in house and

then demonstrate that the method developed is correct.

Possible solutions

NEL will search for local laboratories in which the EPA 1664 Method has previously been developed

and used, and establish costs for analysing a certain number of oil and grease samples.

The two short-listed test organisations to develop the EPA 1664 test method and demonstrate its

competency by either being involved in an inter laboratory comparison study or taking part in oil and

grease proficiency testing that is organised by an established USA organisation.

6.3 Instrument Specific Issues and Problems

6.3.1 Digitrol (Light Scattering)

Details are needed as to how this sensor can be arranged for testing and what impact this may have on

the test fluid properties if the test loop is a recirculation based.

Due to an anticipated pressure drop across the unit and high velocity at the sensor s a o est ope i g, oil droplet breakdown (homogenisation) may occur. A concern was therefore raised by WPG members

that if a recirculation test loop is to be used, the unit may continuously reduce the oil droplet size.

However, this may not be the case, as a recirculation based test loop can reach a steady state quickly.

Further details are to be obtained from Digitrol as to how their early qualification tests were conducted at

Porsgrunn and Brazil.

6.3.2 J M Canty (Microscopy)

It is anticipated that the unit supplied will be an inline system. No particular problem is expected other

than the need of clean water supply at a specific pressure (>65 barg) and flow rate, which will require a

separate pump and pipes.

6.3.3 ProAnalysis (LIF)

A probe based unit is expected, which will be fitted into the test section using a flange. No real issue is

expected if the probe to be supplied is of their existing technology, i.e., not fitted with an automatic

NEL


retraction mechanism designed for conducting ultrasonic cleaning under pressure. This (the supply of an

existing technology) is now confirmed.

6.3.4 Clearview Subsea (CLFM)

The CLFM prototype will be connected on a by-pass line with the test fluid exiting the sensor returning

back to the test loop. Whilst the exact installation of the sensor will depend on the final prototype

configuration, it is not currently anticipated to have any major issues. As the project progresses, this will

become clearer in terms of what is exactly needed regarding connections and requirements for items

such as high pressure cleaning liquid.

6.4 Other Tests

6.4.1 Testing of the Cleaning Mechanism under High Pressure

Testing of cleaning mechanisms under high pressure was not originally planned. Also, such testing may

require the set up of a separate test facility, which will add additional costs to the project.

For the Digitrol unit, the emphasis of its design was on fouling prevention, rather than cleaning. Whilst

Digitrol may not object to testing with a manual foul, it will not be easy to conduct a cleaning test with

the unit under a flow condition and at the same time under high pressure.

For the Canty unit, testing the cleaning mechanism at high pressure will also be a challenging task, as one

has to provide a pressure differential of over 65 barg for the supply of the cleaning fluid at a flow rate of

10 GPM.

For ProAnalysis, it will depend upon the exact unit to be supplied, i.e., whether it will have an automatic

retraction mechanism built in for cleaning purpose.

Clea ie s CLFM p ototype utilizes high p essu e ate hi h is at least psi highe tha the produced water in the sensor.

WPG e e s i put a d o e ts ha e o ee e ei ed follo i g the issui g of the d aft test requirements report. The majority agreed that this is not considered as a priority at the present time for

the Phase 2 bench scale test. Further discussion can also be found in Section 3.2.

6.4.2 Testing Using Real Produced Water

The advantage of using real produced water for testing is obvious. This is why it is always preferable to

have a field trial to see if a particular sensor can work at a particular installation.

However, there are issues and concerns to test sensors in a laboratory environment using transported

produced water. These may include:

Representativeness of the transported produced water due to settling, bacteria action and

degradation

Availability and also transportation of a significant amount of produced water

The latter may be manageable for a recirculation based loop test in which a few cubic meters may be

sufficient, it would be extremely challenging for a once throughput based loop test.

NEL


So the use of real produced water for Phase 2 bench scale testing has been questioned in terms of value

for money and usefulness.

Further WPG members input and comments were sought on the following questions:

Should we use real produced water for testing?

If so, can you possibly help supply the test organization the produced water?

Again majority comments received from WPG members indicated that synthetic / simulated produced

water should be preferred for the bench scale test. Field tests (with real produced water) will be required

anyway at a later stage of sensor development and qualification.

Reference

[1] Produced Water Sensor Development Project – Project Management Plan, RPSEA 12121-6301-03,

November 19, 2014

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