Data-Driven Reservoir Modelingintroduces 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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Data-Driven Reservoir Modeling
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Edition
0Table of contents
- CoverĀ
- Title Page
- Copyright
- Dedication
- Acknowledgments
- Foreword
- ContentsĀ
- 1 Introduction
- 2 Data-Driven Problem Solving
- 3 Reservoir Modeling
- 4 Data-Driven Technologies
- 5 Pitfalls of Using Machine Learning in Reservoir Modeling
- 6 Fact-Based Reservoir Management
- 7 Top-Down Modeling
- 8 The Spatio-Temporal Database
- 9 History Matching the Top-Down Model
- 10 Post-Modeling Analysis of the Top-Down Model
- 11 Examples and Case Studies
- 12 Limitations of Data-Driven Reservoir Modeling
- 13 The Future of Data-Driven Reservoir Modeling
- References
- Index