How to Get Optimal Production Utilizing Machine Learning...PI System data as well as external data...

How to Get Optimal Production

Utilizing Machine Learning

Optimizing Productivity Case Study 4

Artificial Lift Anomaly Detection

1

#PIWorld ©2019 OSIsoft, LLC

Outline

2

Examine the Ability of Machine Learning(ML) to predict

Anomalies on Artificial Lift Equipment to Pre-EmptFailure

and Eliminate unplanned Downtime

A Cross Section Analysis Including Plunger, ESP, and PCP pumps

• A look at how equipment failures can be predicted using MLintelligence

• Apply ML to detect scale, hydrates, and corrosion build up on your artificial lift

• Understanding the ability of ML to predictanomalies

Conclusion

Analytics Types

Applied Machine Learning

We use these processes and technologies because they work at solving real world industrial problems.

Planning & Forecasting

RankingKnowledgeBuilding

#PIWorld

But…• Machine learning needs fuel, that fuel

is data, often a large amounts of data.

• Machine learning workloads

involve

• training models using largescale

historical data sets.

• using these model to make newpredictions.

• ML requires secure, robust & scalable data, data in context.

#PIWorld

Predictive and Monitoring

Data in Context; PI AF Template Used for Well Plunger Analytics


Company faced highly expensive repair costs from electrical submersible pump (ESP)

failures so they wanted to reduce costs through the detection of failure before these pumps breakdow

Tom Hosea, Consultant

n.

• Using predictive insights, moved

from a reactive to predictive

maintenance mode

• A $5K opportunity cost/day/well

that scaled - 10,000 wells (with

a 10% failure rate & average

of 9 days of downtime), the

result is $45 million/yr

ESP Reliability Improvement Program Saves$45M/yr – PI & Azure Machine Learning

Company lacked visibility in predicting failures in electrical submersible pumps leading to high repair costs.

• $100M+/yr in repair of Electrical Submersible Pump (ESP)

• 3,800 ESPs with 20% failure rate per year

• Develop a PI System based application to predict ESP failure

• Setup PI Notifications to generate exception-based reporting

• Created and operationalized a proprietary predictive model that captured 60% of actual failures 60 days in advance


Production Improvement Opportunity

No. of ESP’s

• ~ 3800 installed ESPs

Gas Production

• ~ 6.6 MM BFPD

• ~ 250 M BOPD Gross

Failures

• Failure rate ~ 0.209

• ~ 800 failures/year

US$ 45 Million/Year in failure cost,

exclusive of lost production


ESP Data Object Model Structure


Identification of Valid Business Rule


With PI AF Asset Templates, Create QuickDisplays

• Can drill down to see what went wrong for each pump using context relative templated displays for scalability and ease of use.

• End user can create their own displays


Prescriptive

YPF – iUp intelligent Upstream


iUp Web

Well overview (PI VISION)


Well Torque (PI WebAPI) ESP Curves (PI WebAPI) Goodman (PI WebAPI)

ESRI Maps (PI WebAPI)

iUp Web


Widgets

• Torque

• Goodman

• Pump Curves

• Dyna Cards

iUp Web - Beam Pump Torque

• Minimum Net

Torque

• Minimum

CLF


iUp Web – Beam Pump Goodman

• Metallurgical

stress

• Service Factor


iUp Web – ESP pump curves

• Real-time data

• Operating

window


iUp Web: Dyna card diagnostic


RESULTS

Business Challenge: standardize data countrywide

17K wells in 220 oil fields

CHALLENGE SOLUTION

Collect, transform anddisplay data across the country in a uniform way.

Provide SCADA with template capability and give users awebsite with precise information

Everybody gets the same information, the same way and performs the sameanalysis.

• PI AF

• PI WebAPI

• SQL andOLEDB

• 700+ users

• Engineers moreproductive

• Increase uptime

• Field data collection

• Asset modelling

• Presentation layer

18#PIWorld ©2019 OSIsoft, LLC

Descriptive and Machine Learning

Predictive Maintenance on ESPs


Approach to Build a Model


Data Driven Predictions


• Linked PI data in PI AF with maintenance data in WellView to analyze job cost, runtime and

downtime – identified poorest performing pumps by OEM

• Used text analysis to identify reasons for failures.

• 400 problematic wells. Downhole Heat Exchangers

Diagnostic Analysis on ESPs


• Data scientists derived model features from PI data, static well and ESP attributes

• Features fed to a ensemble of standard ML models within Microsoft Azure Machine Learning Studio

• Data-driven predictive models capable of predicting ESP failure 60 days in the future

Predictive Analysis on ESPs


PI Data

Pump Vibration

Motor Temperature

Drive Frequency

Well Head Pressure

Flow Line Pressure

Pump Inlet Pressure

Pump Inlet

Temperature

Average 3 Phase

Current

Average 3 Phase

Voltage

Drive Output Voltage

Earth Leakage Current

Drive Output Current

Power Factor


Static Well Data

Oil Density

Well Test Basic

Sediments and Water

Well Test Gas Oil Ratio

ESP Metadata

Make

Manufacturer

Location

…

Data in the ESP Models

ESP Decision Tree


• On September 21, 2015 model predicted 6 wells would fail during next 60 days. As of November 5, 2015 (45 days

into prediction) 4 of the 6 wells were offline - 3 wells failed and 1 well had been taken down for maintenance. 60%+

success rate after first training

• Predictive insights were further analyzed in PI Vision and enabled targeted maintenance scheduling

• Predictions allowed outage for maintenance to be reduced from 30 to 21 days per pump across 1,100 wells avoiding

$millions in revenue loss

• Better decisions could be made around future ESP purchases and service contracts

Predictive Analysis on ESPs - Results


Overview: Transforming Data into Actionable Insight

Raw,

collected

dataPI System data as

well as external data

1. OSIsoft PI System on Microsoft Azure

Data-guided

decisions for

greater

operational

efficiency and

business profit

Advanced modeling

with machine

learning

e.g., Time to failure,

performance

forecasts

2. Prepare Data 3. Model Data 4. Visualize Insights

Why has my well declined/died? …

Gas

Rat

e,e

3m

3/d

Wat

er

Rat

e,m

3

30

25

20

15

Gas Rate

Water Rate10

5

0

Date


• Liquid Loading?

• Backpressure?

• Scaling ?

• Hydrates?

• Corrosion?

• Reservoir?

… And how do I get it back on production?

Hydrate/Scale Prediction


Productivity Impairment – Scale…Predictive

(Calcium carbonate, calcium sulfate, barium sulfate mostly)

Shower example

Slotted Liner


Process Interruption & Operational IssuesCase Study: Reduce Process Interruptions & Optimize Assets

Process Pressure Control Fluctuations

Indicate Actual HydrateFormation

Actual Temp ~41 Deg

Hydrate formation Temp ~48 Deg F

24 hours

• Process

Temperature

Increase

• Control Stability

Restored

Background

•

•

•

•

Transports “Wellhead” Gas with High Concentration of

C3+ Hydrocarbons

Hydrates Can Form in “Heavy” or “Wet” Gas Applications

Hydrates Can Interrupt/Block Gas Flow in Piping & Equipment

Eliminate or Affect Hydrate Formation Line Via:

1. Introduction of Inhibitor/Methanol

2. Change Process to Avoid Hydrate Formation Area

Solution

• Leverage PI Analytics to PredictHydrate

FormationTemperature

• Use PI Notifications to Monitor Process/Hydrate

FormationTemperature

• Alert Key Personnel to PotentialHydrate

Formation

• Modify Process Accordingly to Avoid

Interruptions/Upsets

Results


• Modify Operations in Response to Notifications

• Reduce Operating Pressures to Avoid Hydrate

Formation

• Reduced Capacity Vs. CompleteOutage

• Minimize Dependency on Hydrate Inhibitor

• Reduce Costs &Consumption

• Eliminate Secondary Impacts of Inhibitor

PI System in AWS,Interfaces on Premises

IoT

Devices

IoT

Core

Redshift Kinesis

Quicksight

SageMaker Amazon

EMR

Kafka

Athena

S3

PI Integrator for Business Analytics

PI System

Primary network ingress

© Copyright 2019 OSIsoft, LLC© Copyright 2019 OSIsoft, LLC 37

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ConclusionsMachine learning needs large amounts of data

How to Get Optimal Production Utilizing Machine Learning...PI System data as well as external data...

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