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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
Source: Mark Reynolds, compilation
Data Diversity - Spatial, Temporal, Referential](https://image.slidesharecdn.com/2016-04-19machinelearning-160807111804/85/2016-04-19-machine-learning-23-320.jpg)
2016 04-19 machine learning
- 1. Machine Learning April 19, 2016 Roadmap to Constructing A Top Down Machine Learning Paradigm
- 2. 1 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/
- 3. 2 Machine Learning, Deep Learning, AI Roadmap to Constructing a Top Down Machine Learning Paradigm E&P organizations are turning more attention to accumulated data to enhance operating efficiency, 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 system approach will be presented to stimulate out-of-the-box thinking to address the machine learning paradigm.
- 4. 3 Past Paradigm Shifts • Seismic • Horizontal Drilling • Off Shore • Factory Drilling Paradigm Shifts in Process • Big Crew Change • Mobility (anytime, anywhere) • Big Data • Machine Learning The Shifting O&G Paradigm Source: Mark Reynolds, compilation
- 5. 4 Changing Paradigms • Computing Paradigm (4th Paradigm / eScience) • O&G Paradigm (Shale 2.0) New Paradigms • Machine Learning Paradigm Paradigms We Are Discussing Today
- 6. 5 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
- 7. 6 The Shifting Computing Paradigm Descriptive and Formulaic Hypothetical and Investigative Expertise Driven Models and Cases Multivariant Differential Modelling Source: Mark Reynolds, compilation eScience Traditional Science
- 8. 7 The Shifting Computing Paradigms • 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 it Data Exploration Source: Mark Reynolds, compilation
- 9. 8 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”
- 10. 9 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: Mark Reynolds, compilation 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 technologies to support data federation and collaboration ” ~ Jim Grey
- 11. 10 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
- 12. 11 The Machine Learning Paradigm Unsupervised Learning Supervised Learning Semi-Supervised Learning Reinforcement Learning 24/7 Predictive Analytics Data Mining Machine Learning AI Source: Mark Reynolds, compilation
- 13. 12 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 Source: Mark Reynolds, compilation
- 14. 13 Domestic • Nest® Thermostats • Pandora / Amazon • Spam Detection • Fraud Detection • Traffic Light Duty Cycle • Google Upstream O&G • Pump-Jack Duty Cycle (circa 1986) • Closed Loop Directional Drilling (circa 2009) Examples of Machine Learning
- 15. 14 The Bridge Into Machine Learning Today Tomorrow Integrated Systems Engineering
- 16. 15 Integrated Systems Engineering 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 Systems Land and Regulatory Geology Geophysics Drilling Engineering Completion Engineering Production Engineering Reservoir Engineering Systems Engineering Source: Mark Reynolds, compilation
- 17. 16 Integrated Engineering – Top-Down • 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 Source: Mark Reynolds, compilation
- 18. 17 Integrated Engineering – Top-Down • 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
- 19. 18 Integrated Engineering – System Flow Acquire Analyze Annunciate Archive Analyze Anticipate Apply Data Information Visualization Knowledge Forensics Understanding Analysis & Mining Wisdom Anticipating 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 Source: Mark Reynolds, compilation
- 20. 19 Integrated Engineering – Top-Down 19 Data Modeling Proactive & Closed-Loop Systems Mining and Analytics Forensics Control Visualization and Observation Source Capture and Utilization • Intelligence during operations (Observation and Anticipation) • Intelligence reviewing operations (Forensic) • Intelligence planning operations (Historical and Analytical) Source: Mark Reynolds, compilation Well Plan RT Prod RT Drill Geo- steer RT Frac Daily Rpts AFE
- 21. 20 Applied Machine Learning 101 Training Data Pre- Processing Learning Error Analysis Model Learning (Phase 1) Prediction (Phase 2) New Data Model Predictable Result
- 22. 21 Representative Algorithms • 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
- 23. 22 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 Source: Mark Reynolds, compilation Data Diversity - Spatial, Temporal, Referential
- 24. 23 The Fast Data ecosystem in O&G Land Drilling Reservoir Completion Water Production Steering Regulatory Midstream Source: Assorted web images
- 25. 24 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
- 26. 25 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?” )
- 27. 26 Mark Reynolds Mark Reynolds Vitae • Southwestern Energy • Lone Star College • Intent Driven Designs • Scan Systems • Sikorsky Aircraft • General Dynamics • Southwestern Energy Email – Mark_Reynolds@swn.com