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Data Science And Machine Learning Applications In Subsurface Engineering
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Book Synopsis Data Science and Machine Learning Applications in Subsurface Engineering by : Daniel Asante Otchere
Download or read book Data Science and Machine Learning Applications in Subsurface Engineering written by Daniel Asante Otchere and published by CRC Press. This book was released on 2024-02-06 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.
Book Synopsis Machine Learning Applications in Subsurface Energy Resource Management by : Srikanta Mishra
Download or read book Machine Learning Applications in Subsurface Energy Resource Management written by Srikanta Mishra and published by CRC Press. This book was released on 2022-12-27 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy). Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance) Offers a variety of perspectives from authors representing operating companies, universities, and research organizations Provides an array of case studies illustrating the latest applications of several ML techniques Includes a literature review and future outlook for each application domain This book is targeted at practicing petroleum engineers or geoscientists interested in developing a broad understanding of ML applications across several subsurface domains. It is also aimed as a supplementary reading for graduate-level courses and will also appeal to professionals and researchers working with hydrogeology and nuclear waste disposal.
Book Synopsis Advances in Subsurface Data Analytics by : Shuvajit Bhattacharya
Download or read book Advances in Subsurface Data Analytics written by Shuvajit Bhattacharya and published by Elsevier. This book was released on 2022-05-18 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world Offers an analysis of future trends in machine learning in geosciences
Book Synopsis Data Science and Machine Learning Applications in Subsurface Engineering by : Daniel Asante Otchere
Download or read book Data Science and Machine Learning Applications in Subsurface Engineering written by Daniel Asante Otchere and published by CRC Press. This book was released on 2024-02-06 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.
Book Synopsis Machine Learning for Subsurface Characterization by : Siddharth Misra
Download or read book Machine Learning for Subsurface Characterization written by Siddharth Misra and published by Gulf Professional Publishing. This book was released on 2019-10-12 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. Learn from 13 practical case studies using field, laboratory, and simulation data Become knowledgeable with data science and analytics terminology relevant to subsurface characterization Learn frameworks, concepts, and methods important for the engineer’s and geoscientist’s toolbox needed to support
Book Synopsis A Primer on Machine Learning in Subsurface Geosciences by : Shuvajit Bhattacharya
Download or read book A Primer on Machine Learning in Subsurface Geosciences written by Shuvajit Bhattacharya and published by Springer Nature. This book was released on 2021-05-03 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.
Book Synopsis Machine Learning Guide for Oil and Gas Using Python by : Hoss Belyadi
Download or read book Machine Learning Guide for Oil and Gas Using Python written by Hoss Belyadi and published by Gulf Professional Publishing. This book was released on 2021-04-09 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges. Helps readers understand how open-source Python can be utilized in practical oil and gas challenges Covers the most commonly used algorithms for both supervised and unsupervised learning Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques
Book Synopsis Machine Learning and Data Science in the Oil and Gas Industry by : Patrick Bangert
Download or read book Machine Learning and Data Science in the Oil and Gas Industry written by Patrick Bangert and published by Gulf Professional Publishing. This book was released on 2021-03-04 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful Gain practical understanding of machine learning used in oil and gas operations through contributed case studies Learn change management skills that will help gain confidence in pursuing the technology Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)
Book Synopsis Artificial Intelligence for Subsurface Characterization and Monitoring by : Aria Abubakar
Download or read book Artificial Intelligence for Subsurface Characterization and Monitoring written by Aria Abubakar and published by Elsevier. This book was released on 2024-11-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence for Subsurface Characterization and Monitoring provides an in-depth examination of how deep learning accelerates the process of subsurface characterization and monitoring and provides an end-to-end solution. In recent years, deep learning has been introduced to the geoscience community to overcome some longstanding technical challenges. This book explores some of the most important topics in this discipline to explain the unique capability of deep learning in subsurface characterization for hydrocarbon exploration and production and for energy transition. Readers will discover deep learning methods that can improve the quality and efficiency of many of the key steps in subsurface characterization and monitoring. The text is organized into five parts. The first two parts explore deep learning for data enrichment and well log data, including information extraction from unstructured well reports as well as log data QC and processing. Next is a review of deep learning applied to seismic data and data integration, which also covers intelligent processing for clearer seismic images and rock property inversion and validation. The closing section looks at deep learning in time lapse scenarios, including sparse data reconstruction for reducing the cost of 4D seismic data, time-lapse seismic data repeatability enforcement, and direct property prediction from pre-migration seismic data. Focuses on deep learning applications for geoscience provides a one-stop reference for deep learning applications for geoscience Provides comprehensive examples for state-of-art techniques throughout the subsurface characterization workflow Presented applications come with realistic field dataset examples so that readers can learn what to expect in real-life
Book Synopsis A Primer on Machine Learning Applications in Civil Engineering by : Paresh Chandra Deka
Download or read book A Primer on Machine Learning Applications in Civil Engineering written by Paresh Chandra Deka and published by CRC Press. This book was released on 2019-10-28 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB® exercises
Book Synopsis Machine Learning Applications in Civil Engineering by : Kundan Meshram
Download or read book Machine Learning Applications in Civil Engineering written by Kundan Meshram and published by Elsevier. This book was released on 2023-10-01 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Applications in Civil Engineering discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification models for Civil Engineering Applications, Bioinspired Computing models for Civil Engineering, and their case studies. Using this book, civil engineering students and researchers can deep dive into Machine Learning, and identify various solutions to practical Civil Engineering tasks. Introduces various ML models for Civil Engineering Applications that will assist readers in their analysis of design and development interfaces for building these applications Reviews different lacunas and challenges in current models used for Civil Engineering scenarios Explores designs for customized components for optimum system deployment Explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency
Book Synopsis Machine Intelligence and Data Science Applications by : Amar Ramdane-Cherif
Download or read book Machine Intelligence and Data Science Applications written by Amar Ramdane-Cherif and published by Springer Nature. This book was released on 2023-10-03 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications (MIDAS 2022), held on October 28 and 29, 2022, at the University of Versailles—Paris-Saclay, France. The book covers applications in various fields like data science, machine intelligence, image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis. It also includes interdisciplinary applications like legal, healthcare, smart society, cyber-physical system, and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in the machine intelligence discipline, and engineering graduates.
Book Synopsis Proceedings of the Rocscience International Conference 2023 (RIC2023) by : Reginald E. Hammah
Download or read book Proceedings of the Rocscience International Conference 2023 (RIC2023) written by Reginald E. Hammah and published by Springer Nature. This book was released on 2023-12-08 with total page 879 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. Rocscience is delighted to announce the Rocscience International Conference 2023 (RIC2023), an in-person gathering to be held from April 24–26, 2023, in Toronto, Canada. RIC2023's primary objective is to bring geotechnical professionals together to meet and exchange ideas on important issues and developments in geotechnical engineering, particularly combinations of emerging and mature technologies. The geotechnical industry is rapidly evolving. Engineers are more connected through technology, technology is becoming more integrated than ever, and methods combining these technologies are becoming more prevalent. This movement towards combining technologies led us to the conference theme, “Synergy in Geotechnical Engineering – Success Beyond Individual Technologies.” We believe the time is right to highlight how far the industry has come with various technologies and continues to develop. The conference aims to create an environment that fosters new perspectives and helps attendees delve deeper into innovative approaches. During RIC2023, Rocscience will award the 2023 Lifetime Achievement Medal to Dr. Norbert Morgenstern, an internationally recognized authority in the engineering community. As both a practitioner and educator, Dr. Morgenstern’s contributions to the geotechnical community continue to benefit engineers worldwide, and he will give an address on his career. In addition to keynotes by Dr. Morgernstern and four other distinguished speakers, there will be several technical and networking sessions.
Author :Abdolhossein Hemmati-Sarapardeh Publisher :Gulf Professional Publishing ISBN 13 :0128223855 Total Pages :324 pages Book Rating :4.1/5 (282 download)
Book Synopsis Applications of Artificial Intelligence Techniques in the Petroleum Industry by : Abdolhossein Hemmati-Sarapardeh
Download or read book Applications of Artificial Intelligence Techniques in the Petroleum Industry written by Abdolhossein Hemmati-Sarapardeh and published by Gulf Professional Publishing. This book was released on 2020-08-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input
Book Synopsis Machine Learning for Science and Engineering, Volume 1: Fundamentals by : Herman Jaramillo
Download or read book Machine Learning for Science and Engineering, Volume 1: Fundamentals written by Herman Jaramillo and published by SEG Books. This book was released on 2023-04-01 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook teaches underlying mathematics, terminology, and programmatic skills to implement, test, and apply machine learning to real-world problems. Exercises with field data, including well logs and weather measurements, prepare and encourage readers to begin using software to validate results and program their own creative data solutions. As the size and complexity of data soars exponentially, machine learning (ML) has gained prominence in applications in geoscience and related fields. ML-powered technology increasingly rivals or surpasses human performance and fuels a large range of leading-edge research. This textbook teaches the underlying mathematics, terminology, and programmatic skills to implement, test, and apply ML to real-world problems. It builds the mathematical pillars required to thoroughly comprehend and master modern ML concepts and translates the newly gained mathematical understanding into better applied data science. Exercises with raw field data, including well logs and weather measurements, prepare and encourage the reader to begin using software to validate results and program their own creative data solutions. Most importantly, the reader always keeps an eye on the ML’s imperfect data situations as encountered in the real world.
Author :Abdolhossein Hemmati Sarapardeh Publisher :Gulf Professional Publishing ISBN 13 :0128186801 Total Pages :322 pages Book Rating :4.1/5 (281 download)
Book Synopsis Applications of Artificial Intelligence Techniques in the Petroleum Industry by : Abdolhossein Hemmati Sarapardeh
Download or read book Applications of Artificial Intelligence Techniques in the Petroleum Industry written by Abdolhossein Hemmati Sarapardeh and published by Gulf Professional Publishing. This book was released on 2020-09-09 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input
Book Synopsis Applied Statistical Modeling and Data Analytics by : Srikanta Mishra
Download or read book Applied Statistical Modeling and Data Analytics written by Srikanta Mishra and published by Elsevier. This book was released on 2017-10-27 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification. Beginning with a foundational discussion of exploratory data analysis, probability distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal. Authored by internationally renowned experts in developing and applying statistical methods for oil & gas and other subsurface problem domains Written by practitioners for practitioners Presents an easy to follow narrative which progresses from simple concepts to more challenging ones Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications
