2016 03-16 digital energy luncheon

Digital Energy LuncheonMarch 16, 2016

Machine Learning:Fundamentals andE&P Applications

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Introduction to Southwestern Energy

Southwestern Energy Company (NYSE: SWN) is a leading natural gas and oil company with operations predominantly in the United States, engaged in exploration, development and production activities, including related natural gas gathering and marketing.

Source: http://www.swn.com/

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Digital Energy Luncheon

Machine Learning:Fundamentals and E&P Applications

Machine Learning encompasses data acquisition, transmission,

retention, analysis, and reduction. The expected outgrowth of 24x7

data systems and operations centers is Knowledge Engineering and

Data Intensive Analytics AKA Machine Learning. This presentation will

develop and apply Machine Learning concepts to the Upstream O&G

industry. Specific focus will be given to the fundamental concepts and

definitions of Machine Learning along with the application of Machine

Learning.

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Machine Learning

“ A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. ”

~Tom Mitchell

Source: Tom Mitchell, Mitchell, T. (1997). Machine Learning, McGraw Hill.

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Use Case #1 – Lateral Placement

Source: http://geology.com/articles/horizontal-drilling/

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Predictive Analytics• Focuses on Prediction

– Based on Known Properties– Learned from Training Data

Data Mining• Focuses on Discovery

– Unknown Properties in Data– The Analysis Phase of

Knowledge Discovery

Precursors to Machine Learning

Machine Learning is the “Extraction of Wisdom by Understanding the underlying Data”

~Mark Reynolds

Source: Mark Reynolds, compilation

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Machine Learning: Data into Wisdom


Seismic

Drilling

Completions

Production

Data InformationVisualization

KnowledgeForensics

UnderstandingAnalysis &

Mining

WisdomAnticipating Application

RT Frac

Daily Rpts

Well Plan RT

Drill

Geo-steer

AFE

RT Prod

Reservior

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Machine Learning on the Hype Curve

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Use Case #2 – Offset Torque & Drag

Source: Gefei Liu, PVI Connecting Dots with Lines Using Drilling Software, August 20, 2013 http://www.pvisoftware.com/blog/2013/08/

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The Four Paradigms in O&G

• O&G is where we found itEmpirical

• O&G is where we expect itTheoretical

• O&G is where we estimate itComputational

• O&G is where we infer itData Exploration


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The Catalyst• Data captured by

instruments• Data generated by

simulations• Data acquired by

sensor networks

The Destination• Solutions from data analysis• Solutions from data mining• Solutions from visualization• Solutions from drill down• Solutions for bottom line• Solutions using eScience

Machine Learning in the 4th Paradigm

Source: eScience and the Fourth Paradigm: Data-Intensive Scientific Discovery and Digital Preservation, Tony Hey, Microsoft Research http://www.alliancepermanentaccess.org/wp-content/uploads/2011/12/apa2011/15_%28Nov11%29TonyHey-APA%20Meeting.pdf

“ eScience is the set of tools and technologiesto support data federation and collaboration ”

~ Jim Grey

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Machine Learning in the 4th Paradigm

Acquire Analyze Annunciate Archive Analyze Anticipate Apply

Data InformationVisualization

KnowledgeForensics

UnderstandingAnalysis &

Mining

WisdomAnticipating Application

Creating Informational Accessibility and Transparency Discovering Experiential Performance Improvements Segmenting Processes and Process Results Replacing Human Decision w/ Automated Algorithms Innovating New Models, Products, Services


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Modern Data Exploration

Unsupervised Learning

Supervised Learning

Reinforcement Learning

Semi-Supervised Learning

24/7

Predictive Analytics

Machine Learning

Data Mining

AI


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Principal Concepts in Machine Learning

• Unsupervised Learning– Data is unlabeled

• Supervised Learning– Teach and train with data that is well labeled with a

defined output• Reinforcement Learning

– Validity of data alignment is served as feedback• Semi-Supervised Learning

– Some of the data is labeled, some is unlabeled


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Use Case #3 – Unsupervised Learning

Unsupervised Learning Torque increases in the curve


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Textbook Process of Machine Learning

Training Data

Pre-Processing Learning Error

AnalysisModel

Phase 1) Learning

Phase 2) Prediction

New Data ModelPredictable

Result

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Algorithmic Approaches

• Decision Tree Learning– Maps observation to conclusions

• Association Rule Learning– Discovering interesting relations

• Artificial Neural Networks– Incremental function modules

• Inductive Logic Programming– Rule based representations for input

--> output

• Support Vector Machines– Classification and regression

• Clustering– Assignment of observations to

clusters

• Bayesian Networks– Probabilistic models correlating

variables

• Reinforcement Learning– Finds policy to map states to desired

outcome

• Representation Learning– Principal component analysis

• Similarity & Metric Learning– Pairs of examples train others

• Sparse Dictionary Learning– Datum as linear combinations

• Genetic Algorithms– Mimics natural heuristics

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Use Case #4: Compositional Reservoir

SPE 154505

A novel approach for treating the phase stability and phase split problems in compositional reservoir simulation…

~Vassilis Gaganis, et al

Source: SPE 154505: Machine Learning Methods to Speed up Compositional Reservoir Simulation, June 2012

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Machine Learning: The “Data Layer”

• Engineering the Source– Signals, content, and

characterizations• Engineering the Data

– Address errant data– Address valid spurious data– Address data quality

• Engineering the Store– Repository– Recall and Reporting– Representations

Data Acquisition

Data Transmission

Data Retention

Data Analysis

Data Reduction


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Machine Learning: Data Diversity

• Macro (or field-level)– Spatial– Temporal

• Pad (or offset)– Spatial– Temporal

• Well (or wellbore)– Spatial– Temporal

• External– Uploads– Political, Climate, etc

• The 3 Cs of Data Quality– Consistency– Correctness– Completeness– [#4] Currency– [#5] Conformity


Data Diversity - Spatial, Temporal, Referential

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Machine Learning: The “Output Layer”

• Engineering the Store– Data distribution– Data staging

• Engineering the Recall– Simple query– Cube v Matrix

• Engineering the Use Case– Destination: human– Destination: machine

Classification

Regression

Clustering

Density Estimation

Dimensional Reduction

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Use Case #5: Decline Curve Anomaly


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The Fast Data ecosystem in O&G

Land

Drilling

Reservoir Completion

Water

Production

Steering Regulatory

Midstream

Source: Assorted web images

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Security –OPC / Scada / IIoT

Source: Industrial control systems and SCADA cyber-security, 11 August 2014, By Dr Richard Piggin http://eandt.theiet.org/magazine/2014/08/cyber-security-new-battlefront.cfm

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Machine Learning must be Integrated

Systems & Knowledge Engineer

O&G Systems

Control Systems

Remote Systems

Information Systems

Embedded Systems

Robotic Systems

Data Fusion

Real-Time Systems

Look-Back Analysis

Look-Ahead

SystemsLand and Regulatory

Geology Geophysics

Drilling Engineering

Completion Engineering

Production Engineering

Reservoir Engineering

Systems Engineering


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Algorithmic Approaches (revisited)

• Decision Tree Learning– Maps observation to conclusions

• Association Rule Learning– Discovering interesting relations

• Artificial Neural Networks– Incremental function modules

• Inductive Logic Programming– Rule based representations for input

--> output

• Support Vector Machines– Classification and regression

• Clustering– Assignment of observations to

clusters

• Bayesian Networks– Probabilistic models correlating

variables

• Reinforcement Learning– Finds policy to map states to desired

outcome

• Representation Learning– Principal component analysis

• Similarity & Metric Learning– Pairs of examples train others

• Sparse Dictionary Learning– Datum as linear combinations

• Genetic Algorithms– Mimics natural heuristics

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Keep Your Eye on the Prize

Data

Information

Knowledge

Understanding

Wisdom

Application

The question is NOT“How can we … ?”

But instead“What is the objective?”

( or “Why?” )

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And – Keep Your Eye on the Machine

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Mark Reynolds

Mark Reynolds Vitae• Southwestern Energy• Lone Star College• Intent Driven Designs• Scan Systems• Sikorsky Aircraft• General Dynamics

• Southwestern Energy Email– [email protected]

2016 03-16 digital energy luncheon

Data & Analytics