Synopsis
Nowadays data and information that can be converted into data, are ubiquitous. The data from natural, engineered, and social systems are being collected via various channels and devices and come in different shapes and frequencies, which makes application of data analytics tools necessary and unavoidable. Energy industry in general and its petroleum branch in particular collect and manipulate enormous volumes of simulated, experimental, and field data on daily basis to optimize operations, extract higher value from various resources, and reduce their environmental footprint. Data analytics is a booming and rapidly developing discipline in many engineering fields, and petroleum engineering is not an exception. In the last several years, we have experienced an unprecedented growth in application of artificial intelligence (AI) and machine learning (ML) techniques in almost every aspect of our industry starting from drilling automation for all types of reservoirs and ending with optimization of CO2 huff-n-puff operations in liquid-rich shale formations. The number of research studies that apply various data analytics techniques grows so rapidly, that it is almost impossible for a practicing engineer or a lab researcher to dig through the entire volume of articles and conference papers to learn about suitable data analytics methods for their problems. This is the main reason why this book was created. It serves as a “one stop methodology shop” for practicing engineers, researchers, data analytics experts, and energy data enthusiasts to learn about the most interesting and successfully applied methods, and using analogy decide which technique can be applied to their new problem.Data analytics is a vast discipline that encompasses various approaches and techniques that can take book volumes to list and describe. Realizing this, in this book I focus on the leading-edge techniques, known under umbrella terms of AI and ML (and even ML is sometimes considered a part of AI, but rigorous and thorough mathematical classification of these methods is not an objective of this data analytics handbook), and provide the key concepts behind various AI and ML algorithms. Once the reader develops the sense of how these methods manipulate data and what kind of output they can produce, I provide many analogies or examples of problems to which these techniques were applied. Examples from current petroleum literature are the key ingredient of this book because they give the reader the opportunity to match his/her own engineering problem with the most suitable data analytics technique. This book has three main parts: data analytics with AI, data analytics with fuzzy logic, and data analytics with ML. Subsequently, these three main parts are subdivided into subfields where it is needed. For example, the AI data analytics part feature two types of algorithms: evolutionary computation and swarm intelligence.Use this book as a desk manual for data intensive problems if you are a practicing engineer or data analyst, and as a textbook if you are a student or a researcher in the academia. It will give you a broad and yet sufficiently detailed introduction to the growing field of data analytics for engineering applications.
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