Machine Learning in O&G - Amazon S3...The Machine Learning Paradigm Unsupervised Learning Supervised...

Copyright 2017 Southwestern Energy Company. All rights reserved.

Machine Learning in O&G

Road Map to Constructing a Top-Down

Big Data and Machine Learning System

Mark Reynolds

April 19, 2017

1Copyright 2017 Southwestern Energy Company. All rights reserved.

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/


Abstract

Road Map to Constructing a Top-Down Big

Data and Machine Learning System

E&P organizations are turning more attention to

accumulated data to enhance operating efficiencies,

safety and recovery. The computing paradigm is

shifting, the O&G paradigm is shifting and the rise of

the machine learning paradigm requires careful

attention to top-down integrated systems engineering.

A systematic approach will be presented to stimulate

out-of-the-box thinking to address the machine learning

paradigm.


Shifting Computing Paradigm

Shifting O&G Paradigm

Machine Learning Paradigm

Roadmap Into Machine Learning

Source: Mark Reynolds, compilation


The Shifting Computer Paradigm

Descriptive and

Formulaic

Hypothetical and

Investigative

Expertise Driven

Models and Cases

MultivariantDifferential Modelling

eScience

Traditional Science



The Shifting Computer Paradigm

• 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



Past Paradigm Shifts

• Seismic

• Horizontal Drilling

• Off Shore

• Factory Drilling

Paradigm Shifts in Process

• The New Normal

– Economics

– Health Safety Environmental

Regulatory (HSER)

• Big Crew Change

• Mobility (anytime, anywhere)

• Big Data

• Machine Learning

The Shifting Oil and Gas Paradigm



The Machine Learning Paradigm

“ 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.

Mark Reynolds, compilation

Machine Learning is the “Extraction of Wisdom

by Understanding the underlying Data”


The Machine Learning Paradigm

Unsupervised Learning

Supervised Learning

Semi-Supervised Learning

Reinforcement Learning

24/7

Predictive Analytics

Data Mining

Machine Learning

AI

Beware of torque in the curve!

Beware of extreme data vetting!

Beware of rabbit holes!

Beware of over tweaking!



The Fast Data / Data Silo Paradigm in O&G

Land

Drilling

Reservoir Completion

Water

Production

Steering Regulatory

Midstream

Source: Assorted web images


The Problem with Silos

Well maintained, trickle-shared

Organized, compartmented,

ready for end-user

Controlled, managed

centrally dispatched

Silos, yet unified;

data personality intact, yet tightly coupled;

advanced backend, yet accessibleSource: Google images: silos

Ad-Hoc and Unplanned


Continuous Improvement Alone Won’t Be Enough

• “Continuous improvement

never transformed a candle

into a light”

• “Horses have never

improved to the point they

become cars”

Why All of this Machine Learning Matters

• “There is more oil in our

filing cabinets than we’ve

ever pumped”

• “If you want to find the new

oil, look under the old oil”

Quotes and Quips Along the Journey



Real-Time Tactical Response

• Geosteering

• Washout / Packoff

• Synthetic logging

• Wellbore Stability

• Frack-Hit

• Pump-Jack Duty Cycle

• Anticipated Maintenance

Strategic Planning & Assessing

• Development Planning

• NPT Forensics

• Formation Planning

• Completion Planning

• Water Load Analysis

• Environmental Intervention

• Anticipated Maintenance

Machine Learning In Situ



The Problem and Value Statement

• Inadequate (wishful buy-in)

– Machine Learning will increase value

• Improved (intuitive concepts)

– Adaptive pump-jack duty cycle will prevent dry-pumping

• Superior (estimated potential)

– Maintenance prediction will reduce by 3 (annual) overhaul episodes costing $5M

– Improved completion planning will eliminate up to 5 sand trucks per pad

– Development planning improvements will reduce rig idle time 3-10 days per year



Readiness Assessment – Data, Skills, Support



Applying Machine Learning – Focused, yet Agile

Source: The Machine Learning Mastery Method, Jason Brownlee, October 10, 2016, Start Machine Learning

http://machinelearningmastery.com/machine-learning-mastery-method/

Step 1: Adjust Mindset (believe!).

Step 2: Pick a Process (how to get results).

Step 3: Pick a Tool (implementation).

Step 4: Practice on Datasets (put in the work).

Step 5: Build a Portfolio (show your skills).


Common 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 heuristicsSource: Mark Reynolds, compilation


Machine Learning – End-to-End Engineering

Acquire Analyze Annunciate Archive Analyze Anticipate Apply

DataInformationVisualization

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



Wash, Rinse, Repeat


The Machine Learning Process

Source: Introduction to Azure, David Chappel


Machine Learning Cheat Sheet


Validating and Applying Successes

Source: Machine Learning has transformed many aspects of our everyday life, can it do the same for public services?, Natalia Angarita , May 23, 2016, Capgemini

https://www.capgemini.com/blog/insights-data-blog/2016/05/machine-learning-has-transformed-many-aspects-of-our-everyday-life


The Structure of Scientific Revolutions

• Normal Science

– Equilibrium, harmony

• Model Drift

– Outliers cease to be outliers

– Ripples turn to discontinuity

• Model Crisis

– Alternate methods permitted

– Out-of-the-box reconsidered

• Model Revolution

– New model becomes the new-normal

• Paradigm Change

– (Textbooks play catch-up)

Source: Thomas Kuhn, (1962) The Structure of Scientific Revolutions. University of Chicago Press

Mark Reynolds, compilation

Normal Science

Model Drift

(Anomaly)

Model CrisisModel

Revolution

Paradigm Change Kuhn

Cycle


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?” )



Mark Reynolds

Mark Reynolds Vitae

• Southwestern Energy

• Lone Star College

• Intent Driven Designs

• Scan Systems

• Sikorsky Aircraft

• General Dynamics

SWN Email: [email protected]

Machine Learning in O&G - Amazon S3...The Machine Learning Paradigm Unsupervised Learning Supervised...

Documents

Transcript of Machine Learning in O&G - Amazon S3...The Machine Learning Paradigm Unsupervised Learning Supervised...