Shahab D. Mohaghegh
2017
166 pp; Adobe® Digital Edition
57.17 MB
Textbook
ISBN: 978-1-61399-592-1
Society of Petroleum Engineers
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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.  

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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

Dedication
Acknowledgments
Foreword

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

References
Index

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