ambyint.com

August 2017

AI-Driven Production Optimization Platform

Using Torque-Based Data Science to Create a POC

ALRDC Technology Conference Houston, TX

Ambyint Introduction Solution Overview Performing High Quality Data Science Creating the “Lean” POC Results to Date & Next Steps

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Production Optimization Platform (POP) SaaS Software Platform

Optimizing Production Operations through Physics-based Analysis complemented with AI-Driven Monitoring & Control of

Artificial Lift Systems

High Resolution Adaptive Controller (HRAC) Intelligent Devices

Automated & Intelligent Production Optimization

Hardware

Intelligent devices

Integration with existing

automation

Over-the-air firmware updates

End-to-End Technology Platform

Control/Optimize

Edge computing/ analytics

Over-the-air

analytics updates

Real-time physics + data science

Communications

Integrated comms

Push-based architecture

Data compression

& encryption

Software

Cloud platform: monitoring,

operations & optimization

Automated well

diagnosis

Predictive + prescriptive

analytics

Hi-Resolution Adaptive Controllers POP

Ambyint Introduction Solution Overview Performing High Quality Data Science Creating the “Lean” POC Results to Date & Next Steps

Five Key Criteria for High Quality Data Science

1. High resolution, stroke level data 1. Domain expertise to inform

feature engineering 1. Data lake

1. Marked data

1. Continuous feed of new data to

continually validate models

High-Impact Analytics: Need Physics + Data Science

Source: Terry Trieberg, Theta at ALRDC Gas Well Deliquification Workshop 2/22/17. Presentation titled, “Well Production Automation and Diagnostics: Past, Present, and Future”.

Physics-based Analysis

traditional lift optimization

Data Science & Artificial Intelligence

“big data” statistics

Dover/XSPOC

Weatherford

GE/Lufkin

Flutura

Spark Cognition

Ambyint

XSPOC considerations for modern analytics: 1) domain expertise (physics) and 2) “analytics limited by low quality data”

Deep physics-based analytics expertise + proprietary hardware to generate better data 75MM well operating hours → the equivalent of 214, 40-yr PEs

Deploying actionable modern data science and AI in artificial lift

SCADA Data Limits Data Science

Ambyint Stroke Based Observation

SCADA Fixed Polling Frequency

High Resolution + Stroke Level +

Event-based Data Enables High Quality Data

Science

Sticking?

Tagging?

Lo

ad

Time

Lo

ad

Time

Pumpjack duty cycle

Ambyint

SCADA Low resolution,

incomplete data set without context or

insights

SCADA Data Limits Data Science

Ambyint:

SCADA:

… years of data gathered from...

Exceptional Foundation

1000+

33M+

70/30

10+ … oil wells

… dynamometer cards generated with expert classification

… percentage of horizontal / vertical wells in our data lake

5 ms … sampling rate of high resolution data

https://static.pexels.com/photos/477751/pexels-photo-477751.jpeg

Ambyint Introduction Solution Overview Performing High Quality Data Science Creating the “Lean” POC Results to Date & Next Steps

Lean Means Fewer Sensors

http://www.energylandscapes.net/images/xl/AF4A9804-III-rev-II-web.jpg

Data Science Fills the Role This Equipment Played On Site: ● Load cell ● Position sensor ● POC ● Communications

By Analyzing Electric Motor Torque:

Creating a POC from an Electric Motor

Electric Prime Mover

Current

Torque

Fillage

● Stroke level data created and collected

● Computational mathematics applied to frame stroke algorithms of torque

● Performed at the edge allow us to collect high frequency data

● Data science informed by our physics based features is applied

○ E.g. rod string, PU geometry, motor size

● Machine learning models generated around features of torque patterns

○ 33mm dynocards in data lake; 22mm having torque

○ Modeling/analytics performed at edge and in big data clusters 1

2

1

2

Ambyint approach

High resolution stroke-level data to enable data science

Data lake to develop actionable data science

Domain expertise to inform feature engineering & model development

Intelligent devices to implement actions

Electric Motor = High Resolution Load Cell

Torque and load respond in a similar fashion as fillage decreases Electric motors produce high resolution, high precision, machine level data Relating torque curves to dynamometer data under different pump conditions (rod configuration, pumping unit geometry) allows for a comprehensive model to be built

Full Full

Incomplete Fillage

Incomplete Fillage

Torque Dynamometer

Marking Pumpoff in Torque Trends

100% fillage - Full Card 78% fillage 56% fillage

40% fillage 27% fillage

Analysis: 1. Area under the curve for Peak 1 is

fairly consistent.

2. The transition from Peak 1 to Peak 2

eventually hits the zero torque line as

fillage decreases.

3. Peak 2 area under the curve is much

smaller and to the right in stroke.

4. Points 2 and 3 can be seen as similar

to concave signatures in a surface or

downhole dynocard.

Torque

Peak 1 Peak 2

RPM

Ambyint Introduction Solution Overview Performing High Quality Data Science Creating the “Lean” POC Results to Date & Next Steps

Data Science Results Leveraging Data Lake

Strong linear relationship to fillage via applied cluster analysis techniques

Machine learning models use:

● Torque

● Inferred position

● Well configuration

(physics) parameters

7 months of R&D using 600 wells, 10 years of well data, & 18 million dynocards

Torque to Fillage Model Prediction Error

● Accuracy above internal threshold using data lake ● By comparison, Lufkin’s fillage calculation is generally 80-85% accurate ● Moving to field trials August 2017

10^5 orders of magnitude greater occurrence of

accurate result

Brian Arnst Customer Success Manager +1 210 216 8264 brian.arnst@ambyint.com