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.
