SEISMIC-BASED PREDICTION TECHNOLOGIES SHALE GAS SW

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This book is a useful guide for researchers involved in the technological innovation and production of shale gas exploration and development. It offers a thorough understanding of seismic technologies and their application in shale gas exploration and extraction. This book comprehensively and systematically presents the significance of seismic technologies in predicting shale gas sweet spots. It introduces state-of-the-art seismic-based prediction technologies as well as case studies showcasing their implementation in primary shale gas production areas in China. Innovativeness is one of the highlights of this book. Cutting-edge technologies, such as AI applied in identifying shale gas sweet spots, and achieving excellent results in shale gas production are presented. Readers will gain insights into the latest methodologies, models, and real-world examples, equipping them with the necessary tools to navigate the complex landscape of shale gas resources.

Fundamentals of Machine Learning for Predictive Data Analytics (2版)

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Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies 作者： Kelleher, John D.,Mac Namee, Brian,D’Arcy, Aoife 原文出版社：MIT Press 出版日期：2020/10/20 語言：英文 簡介 The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.