Machine Learning And Data Science In The Oil And Gas Industry
DOWNLOAD
Download Machine Learning And Data Science In The Oil And Gas Industry PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning And Data Science In The Oil And Gas Industry book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Machine Learning And Data Science In The Oil And Gas Industry
DOWNLOAD
Author : Patrick Bangert
language : en
Publisher: Gulf Professional Publishing
Release Date : 2021-03-04
Machine Learning And Data Science In The Oil And Gas Industry written by Patrick Bangert and has been published by Gulf Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-04 with Science categories.
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)
Machine Learning In The Oil And Gas Industry
DOWNLOAD
Author : Yogendra Narayan Pandey
language : en
Publisher: Apress
Release Date : 2020-11-03
Machine Learning In The Oil And Gas Industry written by Yogendra Narayan Pandey and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-03 with Computers categories.
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.
Machine Learning And Data Science In The Power Generation Industry
DOWNLOAD
Author : Patrick Bangert
language : en
Publisher: Elsevier
Release Date : 2021-01-18
Machine Learning And Data Science In The Power Generation Industry written by Patrick Bangert and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-18 with Technology & Engineering categories.
Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study-driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting.
Shale Analytics
DOWNLOAD
Author : Shahab D. Mohaghegh
language : en
Publisher: Springer
Release Date : 2017-02-09
Shale Analytics written by Shahab D. Mohaghegh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-09 with Technology & Engineering categories.
This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations.
Applied Statistical Modeling And Data Analytics
DOWNLOAD
Author : Srikanta Mishra
language : en
Publisher: Elsevier
Release Date : 2017-10-27
Applied Statistical Modeling And Data Analytics written by Srikanta Mishra and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-27 with Science categories.
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
Enhance Oil And Gas Exploration With Data Driven Geophysical And Petrophysical Models
DOWNLOAD
Author : Keith R. Holdaway
language : en
Publisher: John Wiley & Sons
Release Date : 2017-10-09
Enhance Oil And Gas Exploration With Data Driven Geophysical And Petrophysical Models written by Keith R. Holdaway and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-09 with Business & Economics categories.
Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. Apply data-driven modeling concepts in a geophysical and petrophysical context Learn how to get more information out of models and simulations Add value to everyday tasks with the appropriate Big Data application Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.
Machine Learning For Subsurface Characterization
DOWNLOAD
Author : Siddharth Misra
language : en
Publisher: Gulf Professional Publishing
Release Date : 2019-10-12
Machine Learning For Subsurface Characterization written by Siddharth Misra and has been published by Gulf Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-12 with Technology & Engineering categories.
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
A Practical Introduction To Econometric Methods
DOWNLOAD
Author : Patrick K. Watson
language : en
Publisher:
Release Date : 2002
A Practical Introduction To Econometric Methods written by Patrick K. Watson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Business & Economics categories.
The text is aimed at final-year undergraduate students or those at the graduate level doing econometrics for the first time. It is an introductory course in the theory and practice of classical and modern econometric methods. A proper study of the material will allow the reader to - Understand the scope and limitations of classical and modern econometric techniques - Read, write and properly interpret articles and reports of an applied econometric nature - Build upon the elements of econometric theory and practice introduced in the book Although some basic knowledge of matrix algebra and elementary statistical theory will be assumed, much of it is covered in the body of the text. All the main theoretical concepts are illustrated with the use of econometric software, mainly EViews.
Machine Learning For Energy Systems
DOWNLOAD
Author : Denis Sidorov
language : en
Publisher: MDPI
Release Date : 2020-12-08
Machine Learning For Energy Systems written by Denis Sidorov and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-08 with Technology & Engineering categories.
This volume deals with recent advances in and applications of computational intelligence and advanced machine learning methods in power systems, heating and cooling systems, and gas transportation systems. The optimal coordinated dispatch of the multi-energy microgrids with renewable generation and storage control using advanced numerical methods is discussed. Forecasting models are designed for electrical insulator faults, the health of the battery, electrical insulator faults, wind speed and power, PV output power and transformer oil test parameters. The loads balance algorithm for an offshore wind farm is proposed. The information security problems in the energy internet are analyzed and attacked using information transmission contemporary models, based on blockchain technology. This book will be of interest, not only to electrical engineers, but also to applied mathematicians who are looking for novel challenging problems to focus on.
Integrated Operations In The Oil And Gas Industry Sustainability And Capability Development
DOWNLOAD
Author : Rosendahl, Tom
language : en
Publisher: IGI Global
Release Date : 2012-08-31
Integrated Operations In The Oil And Gas Industry Sustainability And Capability Development written by Rosendahl, Tom and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-31 with Business & Economics categories.
The predicted ICT revolution has gained increasing attention in the oil industry the last few years. It is enabled by the use of ubiquitous real time data, collaborative techniques, and multiple expertises across disciplines, organizations and geographical locations. Integrated Operations in the Oil and Gas Industry: Sustainability and Capability Development covers the capability approach to integrated operations that documents research and development in the oil industry. By capability, we refer to the combined capacity and ability to plan and execute in accordance with business objectives through a designed combination of human skills, work processes, organizational change, and technology. This book will serve as a knowledge base for those who are interested in learning about, and those involved in, Integrated Operations in the Oil and Gas Industry.