Machine Learning in the Oil and Gas Industry: Including Geosciences, Reservoir Engineering, and Production Engineering with Python

Machine Learning in the Oil and Gas Industry: Including Geosciences, Reservoir Engineering, and Production Engineering with Python

Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. This leads to a survey of some interesting problems, which are good candidates for applying machine and deep learning approaches. The initial chapters provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industry covers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and production engineering. Throughout the book, the emphasis is on providing a practical approach with step-by-step explanations and code examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. What You Will Learn Understanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industry - Get the basic concepts of computer programming and machine and deep learning required for implementing the algorithms used - Study interesting industry problems that are good candidates for being solved by machine and deep learning - Discover the practical considerations and challenges for executing machine and deep learning projects in the oil and gas industry Who This Book Is For Professionals in the oil and gas industry who can benefit from a practical understanding of the machine and deep learning approach to solving real-life problems. Apply machine and deep learning to solve some of the challenges in the oil and gas industry. The book begins with a brief discussion of the oil and gas exploration and production life cycle in the context of data flow through the different stages of industry operations. This leads to a survey of some interesting problems, which are good candidates for applying machine and deep learning approaches. The initial chapters provide a primer on the Python programming language used for implementing the algorithms; this is followed by an overview of supervised and unsupervised machine learning concepts. The authors provide industry examples using open source data sets along with practical explanations of the algorithms, without diving too deep into the theoretical aspects of the algorithms employed. Machine Learning in the Oil and Gas Industry covers problems encompassing diverse industry topics, including geophysics (seismic interpretation), geological modeling, reservoir engineering, and production engineering. Throughout the book, the emphasis is on providing a practical approach with step-by-step explanations and code examples for implementing machine and deep learning algorithms for solving real-life problems in the oil and gas industry. You will: Understanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industry - Get the basic concepts of computer programming and machine and deep learning required for implementing the algorithms used - Study interesting industry problems that are good candidates for being solved by machine and deep learning - Discover the practical considerations and challenges for executing machine and deep learning projects in the oil and gas industry Yogendra Pandey is a senior product manager at Oracle Cloud Infrastructure. He has more than 14 years of experience in orchestrating intelligent systems for the oil and gas, utilities, and chemical industries. He has worked in different capacities with oil and gas, and utilities companies, including Halliburton, ExxonMobil, and ADNOC. Yogendra holds a bachelor’s degree in chemical engineering from the Indian Institute of Technology (BHU), and a PhD from the University of Houston, with specialization in high-performance computing applications to complex engineering problems. He served as an executive editor for the Journal of Natural Gas Science and Engineering. Also, he has authored/co-authored more than 25 peer-reviewed journal articles, conference publications, and patent applications. He is a member of the Society of Petroleum Engineers. Ayush Rastogi is a data scientist at BPX Energy, Denver CO. His research interests are based on multi-phase fluid flow modeling and integrating physics-based and data-driven algorithms to develop robust predictive models. He has published his work in the field of machine learning and data-driven predictive modeling in the oil and gas industry. He has previously worked with Liberty Oilfield Services in the technology team in Denve