Overview

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. Data-Driven Reservoir Modeling is intended to introduce a technology that is relatively new to petroleum engineers and geoscientists whose day-to-day job responsibilities always bring them to junctures where critical technical decisions need to be made and strategies need to be established. The technology covered in this book adds another decision-making tool to the arsenal of upstream technologists of the petroleum industry. This book should also be useful to petroleum engineering and geosciences undergraduate students in their junior or senior year, as well as to graduate students with some degree of exposure to the principles of petroleum engineering field operations, petroleum geology, and petroleum geophysics. The aim of this book is to present a methodology that is rather new to the petroleum engineering community and is particularly suited to the application of data analytics to physical problems of reservoir engineering for tracking the state of dynamics with the goal of strengthening the decision-making process. With the help of the pragmatic approach provided in this book, data-driven modeling can be effectively used in field planning and development studies.

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9781613995600

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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).

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