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

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-6093-7 . For more detailed information, please visit http://www.apress.com/source-code .

This book is for anyone interested in applications of machine learning in the oil and gas industry. The contents of the book are organized to provide foundational knowledge of computer programming, the implementation of machine learning and deep learning algorithms, and the ways and means to use them for solving oil and gas industry problems. This book does not provide production-quality codes, but it equips you with information on how you can start building a machine learning solution for the oil and gas industry problems discussed in this book and beyond. This book is organized into eight chapters, as follows.

Chapter overviews the different segments of the oil and gas industry and provides example applications of machine learning to upstream industry problems.

Chapter is a brief primer on the Python programming language. You become familiar with the basic syntaxes of the Python programming language. Afterward, you learn about Python libraries, such as NumPy, pandas, and Matplotlib, which are useful for certain machine learningrelated tasks.

Chapter overviews machine learning and deep learning concepts. In this chapter, you learn about supervised and unsupervised machine learning concepts. You work on examples using simplistic and clean datasets. The scikit-learn and TensorFlow libraries are used for implementing machine learning and deep learning code samples, respectively.

Chapter focuses on the application of machine learning to problems in seismic processing and interpretation. Using seismic data available from open data sources, we implement automated salt interpretation. Without going into implementation details, strategies for solving other seismic interpretation problems are briefly discussed.

Chapter focuses on the problems related to geological modeling, including supervised machine learning for estimation of the petrophysical properties away from well locations.

Chapter focuses on approaches for developing machine learning models in decline curve analysis and the use of these models in production forecasting.

Chapter covers production modeling using machine learning methodologies, including production optimization. Part of the chapter provides a methodology for virtual metering and the formulation of virtual sensors.

Chapter discusses opportunities, challenges, and expected future trends. In this chapter, we discuss the challenges encountered when executing machine learningbased digital transformation projects in the oil and gas industry. The potential pitfalls that lead to project failure and ways to avoid them are discussed. Also, the opportunities that inherently lie in addressing these challenges are discussed from both an executives and a practitioners perspective. A roadmap of the machine learningenabled digital transformation of the upstream industry over the coming years is provided.