Sathish Sankaran (Editor and Co-Chair), Sebastien Matringe (Editor and Co-Chair), Mohamed Sidahmed, Luigi Saputelli, Xian-Huan Wen, Andrei Popa, Serkan Dursun
2020
95 pgs; Adobe® Digital Edition
ISBN: 978-1-61399-823-6

Description 

Publishing October 2020!
The FIRST book to publish in the PetroBriefs series, Data Analytics in Reservoir Engineering will be FREE for all 2020 SPE Members!  To be alerted when available for download or purchase, simply email books@spe.org.

Please review our Adobe Digital Edition Checklist prior to download/purchase. 

Data analytics is fundamentally transforming industries. In recent years, there is a growing application of digital technologies in oil and gas exploration and production. At the same time, oil and gas operations are becoming increasingly complicated with modern facility infrastructures, complex reservoirs, increased regulatory requirements, changing workforce demographics, the fast pace of unconventional-field development and ultimately with much more competition.  Data Analytics in Reservoir Engineering focuses on how best to use data analytics to transform the decision-making process in characterizing reservoir parameters, model reservoir behavior and forecast performance.

Coming Soon in the PetroBriefs Series
  
Waterflooding: Chemistry 
  Waterflooding: Facilities and Operations 
  Waterflooding: Design and Development 
  Waterflooding: Surveillance and Remediation
  Waterflooding: Injection Regime and Injection Wells

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Sathish Sankaran is EVP of Engineering and Technology at Xecta Digital Labs. Prior to that he served as Engineering Manager of Advanced Analytics and Emerging Technology for Anadarko Petroleum Corporation. His work focuses on modeling and optimizing hydrocarbon production from reservoir to process plant, with emphasis on blending physics and data-driven methods.

Sebastien Matringe is currently Principal Advisor for Subsurface Technologies at Hess Corporation. He previously held various leadership and engineering positions at Newfield Exploration, Quantum Reservoir Impact and Chevron. He holds a "Diplome d'ingenieur" in Fluid Mechanics from ENSEEIHT in France and MS and PhD degrees in Petroleum Engineering from Stanford University.

Mohamed Sidahmed serves as Machine Learning and Artificial Intelligence R&D Manager for Shell. He also serves as Program Evaluator for ABET, dedicated STEM PEV contributing to the profession. There he contributes to the quality of technical education in collaboration with professionals from academia, industry and government.

Xian-Huan Wen is a Chevron Fellow and the Team Leader of Reservoir Simulation and Optimization Research in Chevron Energy Technology Company. Wen holds a PhD degree in Water Resources Engineering from the Royal Institute of Technology, Sweden and a PhD degree in Civil Engineering from the Technical University of Valencia, Spain. Wen has authored or coauthored more than 80 papers and holds two US patents.

Luigi Saputelli serves as a Reservoir Engineering Expert Advisor to the Abu Dhabi National Oil Company (ADNOC) with 30 years of experience. He has held various positions as Reservoir Engineer (integrated reservoir modeling, simulation, improved oil recovery projects, field development, researcher), Drilling Engineer (drilling and well planning projects, drilling rig automation) and Production Engineer (production modeling, engineering and operations workflow automation projects), in various operators and services companies around the world including PDVSA, Hess and Halliburton.

Andrei Popa currently serves as Reservoir Management Consultant within the Upstream Capability, Reservoir Management Framework Team. In addition to working for Chevron, Andrei has served as an Adjunct Associate Professor at University of Southern California for the last 11 years, where he has taught Applied Reservoir Engineering and Advanced Natural Gas Engineering. He has more than 23 years of experience leading artificial intelligence and machine learning projects and cross-functional teams focused on delivering complex optimization solutions and
conceptual models across Chevron enterprise.

Serkan Dursun is Leader of Artificial Intelligence Center of Excellence CoP in Hydrocarbon Management Division at Saudi Aramco. He was Principal Data Scientist at Marathon Oil, Data Scientist at Schlumberger and Senior Technologist at Halliburton. He served as Adjunct Professor at University of Houston. He has 11 publications in IEEE & SPE and holds 9 US patents. He has special interest in Artificial General Intelligence (AGI), Conversational AI, and Explainable AI (XAI).  

Peer Reviewers -  Eduardo Gildin, Hector Klie, Shahab Mohaghegh, Suryansh Purwar

Preface
About the Authors

1. Introduction 2
1.1. Objectives 2
1.2. Organization of this Book 2
1.3. Background 2
1.4. What Is Data Analytics? 4
1.5. What Is New in Data Analytics? 5
1.6. What Value Can Data Analytics Create for the Oil and Gas Industry? 5
1.7. What Are the Challenges? 6

2. Data-Driven Modeling Methodology 7
2.1. Modeling Strategies 8
2.2. Model Development 11
2.3. Enabling Technologies 11
2.4. Uncertainty Quantification and Mitigation 12

3. Decision Making with Data-Driven Models 13
3.1. Value Creation 14
3.2. Organizational Model 15
3.3. Execution 15

4. Reservoir Engineering Applications 16
4.1. Fluid Pressure/Volume/Temperature 16
4.2. Core Analysis 20
4.3. Reserves and Production Forecasting 22
4.3.1. Resource and Reserves Calculations 22
4.3.2. Production Forecasting 26
4.4. Reservoir Surveillance and Management 32
4.4.1. Reservoir Surveillance 32
4.4.2. Reservoir Management 34
4.5. Enhanced Oil Recovery and Improved Oil Recovery 38
4.5.1. Screening Tools for EOR/IOR 38
4.5.2. Waterflood Management 39
4.5.3. Steamflood Management 42
4.6. Reservoir Simulation 45
4.6.1. Proxy Modeling 45
4.6.2. Reduced-Order Modeling 51
4.6.3. Reduced-Physics Model 54
4.6.4. Predictive Uncertainty Analysis 54
4.6.5. Data-Driven Physics-Based Predictive Modeling 56
4.7. Unconventionals 60
4.7.1. Data Collection 61
4.7.2. Machine Learning 64

5. Future Trends 65
5.1. Data 65
5.2. Field Automation 66
5.3. Applications 67
5.4. People 68
6. References 69
Appendix A—Model Development Process 90

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