Data-Driven Reservoir Modeling

Shahab D. Mohaghegh

• Publisher: Society of Petroleum Engineers
• Year: 2017
• eBook ISBN: 9781613995600
• Edition: 0

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.

Table of contents

• Cover 
• Title Page
• Copyright
• Dedication
• Acknowledgments
• Foreword
• Contents 
• 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