Description
| Product ID: | 9781484260937 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | US |
| Title: | Machine Learning in the Oil and Gas Industry |
| Subtitle: | Including Geosciences, Reservoir Engineering, and Production Engineering with Python |
| Authors: | Author: Ayush Rastogi, Yogendra Narayan Pandey, Srimoyee Bhattacharya, Sribharath Kainkaryam, Luigi Saputelli |
| Page Count: | 300 |
| Subjects: | Programming and scripting languages: general, Programming & scripting languages: general, Machine learning, Machine learning |
| Description: | Select Guide Rating 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 LearnUnderstanding the end-to-end industry life cycle and flow of data in the industrial operations of the oil and gas industryGet the basic concepts of computer programming and machine and deep learning required for implementing the algorithms usedStudy interesting industry problems that are good candidates for being solved by machine and deep learningDiscover 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.
What You Will Learn
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. |
| Imprint Name: | APress |
| Publisher Name: | APress |
| Country of Publication: | GB |
| Publishing Date: | 2020-11-03 |
