Shahab D. Mohaghegh
166 pp.; Softcover
ISBN: 978-1-61399-560-0
Society of Petroleum Engineers
(4 reviews)
Price USD 240.00Retail USD 240.00Add to CartAdd to Wishlist


SPE Member Price USD 120

Data-Driven Reservoir Modeling
introduces new technology and protocols (intelligent systems) that teach the reader how to apply data analytics to solve real-world, reservoir engineering problems.  The book describes how to utilize machine-learning-based algorithmic protocols to reduce large quantities of difficult-to-understand data down to actionable, tractable quantities.  Through data manipulation via artificial intelligence, the user learns how to exploit imprecision and uncertainty to achieve tractable, robust, low-cost, effective, actionable solutions to challenges facing upstream technologists in the petroleum industry.  

Watch a video of Shahab D. Mohaghegh describing his new book, here. 

You may wish to consider these related SPE training courses: 
Shale Analytics
Data Driven Reservoir Modeling
SPE Petroleum Data Analytics Series Week One: Subsurface Analytics
Applied Atatistical Modeling and Data Analytics for Reservoir Performance Analysis


Average Rating:
(based on 4 reviews)

Showing 4 Reviews:

by keith
on 2/20/2020
Data-Driven Reservoir Modeling
Professor Shahab Mohaghegh, being one of the most innovative and experienced thought leaders in the field of data-driven modeling in the upstream, has written a comprehensive and readable book that finally puts to bed the persistent complaints in the industry: where is the proof to substantiate and operationalize these data-driven reservoir models? In his exceptional style, he has expressed clearly the potentially huge business value by focusing on repeatable and scalable data-driven technologies, enhanced by both his domain expertise and career in applying soft-computing processes to the disparate spatial and temporal upstream datasets
by Stephen
on 3/20/2018
Practical, Informative and well written
This is the most expensive page for page book I have ever bought and that's not even including the overseas shipping. But the wisdom shared is invaluable. It has saved me a huge amount of time that would have been wasted going up blind alleys trying to find useful applications of Artificial Intelligence, "Big Data", "Applied Statistics", etc. in the analysis of oilfield data. This book is specifically about the Reservoir Modelling application of Artificial Intelligence. It is not a theoretical textbook or an algorithm recipe book. The explanations of the technology and the suggestions for building "Data Driven Models" provided by the author seem to be distilled from a career's worth of experience and the lessons learned from using this technology. There are some frank discussions about reservoir modelling techniques: Analytical, Empirical, Numerical and Data Driven. The writing style and the diagrams made this book enjoyable as a straight read and easily understandable.
by Hector Klie
on 7/25/2017
Data-Driven Reservoir Modeling
"This book is an intuitive and accessible guide for understanding the fundamentals and business implications of data-driven reservoir modeling in oil & gas. The author provides a clear and provocative message that should enlighten avid engineers to embrace data analytics as a powerful means for bridging the physics of porous media flow with field data measurements. Anyone interested in the digital future of oil fields would greatly benefit from reading this book." - Hector Klie, CEO,
by Thomas Graf
on 7/20/2017
Data-Driven Reservoir Modeling
Professor Mohaghegh describes in an easy understandable way latest technology advances in knowledge-based intelligent algorithms. I strongly believe that those are and will be impacting the work of engineers in field development planning and bringing their subsurface work closer to production operations. His book is a must read for all reservoir engineers that will allow them to thrive with the next generation of decision-making tools.
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Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry,  is the president and CEO of Intelligent Solutions, Inc. (ISI) and professor of Petroleum and Natural Gas Engineering at West Virginia University. He holds BS, MS, and PhD degrees in petroleum and natural gas engineering.

He has authored more than 170 technical papers and carried out more than 60 projects for NOCs and IOCs. He is a SPE Distinguished Lecturer and has been featured in the Distinguished Author Series of SPE’s Journal of Petroleum Technology (JPT) four times. He is the founder of Petroleum Data-Driven Analytics, SPE’s Technical Section dedicated to machine learning and data mining. He has been honored by the US Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of US Secretary of Energy’s Technical Advisory Committee on Unconventional Resources (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage (2014-2016).

Table of Contents


1 Introduction
1.1 Reservoir Models for Reservoir Management
1.2 What Is Top-Down Modeling?
1.2.1 Role of Physics and Geology
1.2.2 Formulation and Computational Footprint
1.2.3 Expected Outcome of a Top-Down Model
1.2.4 Limitations of TDM
1.2.5 Software Tool for the Development of TDM
1.3 Paradigm Shift
1.3.1 Drilling Operation
1.3.2 Mature Fields
1.3.3 Smart Completions, Smart Wells, and Smart Fields
1.3.4 Production From Shale Assets
1.3.5 Reservoir Simulation Models

2 Data-Driven Problem Solving
2.1 Misunderstanding Data-Driven Reservoir Modeling

3 Reservoir Modeling

4 Data-Driven Technologies
4.1 Data Mining
4.2 Artificial Intelligence
4.3 Artificial Neural Networks
4.3.1 Structure of a Neural Network
4.3.2 Mechanics of Neural Network Operation
4.4 Fuzzy Logic
4.4.1 Fuzzy Set Theory
4.4.2 Approximate Reasoning
4.4.3 Fuzzy Inference

5 Pitfalls of Using Machine Learning in Reservoir Modeling

6 Fact-Based Reservoir Management
6.1 Empirical Models in the E&P Industry
6.1.1 Decline Curve Analysis
6.1.2 Capacitance/Resistance Modeling

7 Top-Down Modeling
7.1 Components of a Top-Down Model
7.2 Formulation and Computational Footprint of TDM
7.3 Curse of Dimensionality
7.4 Correlation Is Not the Same as Causation
7.5 Quality Control and Quality Assurance of the Data
7.5.1 Inspecting the Quality of the Data
7.5.2 QC of the Production Data

8 The Spatio-Temporal Database
8.1 Static Data
8.2 Dynamic Data
8.3 Well Trajectory and Completion Data
8.3.1 Two-Dimensional vs. Three-Dimensional Reservoir Modeling
8.4 Resolution in Time and Space
8.4.1 Resolution in Space
8.4.2 Resolution in Time
8.5 Role of Offset Wells
8.6 Structure of the Spatio-Temporal Database
8.7 Required Quantity and Quality of Data

9 History Matching the Top-Down Model
9.1 Practical Considerations During the Training of a Neural Network
9.1.1 Selection of Input Parameters
9.1.2 Partitioning the Data Set
9.1.3 Structure and Topology
9.1.4 The Training Process
9.1.5 Convergence
9.2 History-Matching Schemes in TDM
9.2.1 Sequential History Matching
9.2.2 Random History Matching
9.2.3 Mixed History Matching
9.3 Validation of the Top-Down Model
9.3.1 Material Balance Check

10 Post-Modeling Analysis of the Top-Down Model
10.1 Forecasting Oil Production, GOR, and WC
10.2 Production Optimization
10.2.1 Choke-Setting Optimization
10.2.2 Artificial-Lift Optimization
10.2.3 Water-Injection Optimization
10.3 Reservoir Characterization
10.4 Determination of Infill Locations
10.5 Recovery Optimization
10.6 Type Curves
10.7 Uncertainty Analysis
10.8 Updating the Top-Down Model

11 Examples and Case Studies
11.1 Case Study No. 1: A Mature Onshore Field in Central America
11.2 Case Study No. 2: Mature Offshore Field in the North Sea
11.3 Case Study No. 3: Mature Onshore Field in the Middle East
11.3.1 Data Used During the Top-Down-Model Development
11.3.2 Top-Down-Model Training and History Matching
11.3.3 Post-Modeling Analysis
11.3.4 Performing a “Stress Test” on the Top-Down Model

12 Limitations of Data-Driven Reservoir Modeling

13 The Future of Data-Driven Reservoir Modeling


Preview Data-Driven Reservoir Modeling by downloading the PDF below.