Understanding Data Analytics And Predictive Modelling In The Oil And Gas Industry

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Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry

Author : Kingshuk Srivastava
Publisher : CRC Press
ISBN 13 : 1000995119
Total Pages : 187 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry by : Kingshuk Srivastava

Download or read book Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry written by Kingshuk Srivastava and published by CRC Press. This book was released on 2023-11-20 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies. Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas.

Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry

Author : Kingshuk Srivastava
Publisher :
ISBN 13 : 9781003357872
Total Pages : 0 pages
Book Rating : 4.3/5 (578 download)

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Book Synopsis Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry by : Kingshuk Srivastava

Download or read book Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry written by Kingshuk Srivastava and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies. Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas.

Shale Analytics

Author : Shahab D. Mohaghegh
Publisher : Springer
ISBN 13 : 3319487531
Total Pages : 287 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Shale Analytics by : Shahab D. Mohaghegh

Download or read book Shale Analytics written by Shahab D. Mohaghegh and published by Springer. This book was released on 2017-02-09 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Harness Oil and Gas Big Data with Analytics

Author : Keith R. Holdaway
Publisher : John Wiley & Sons
ISBN 13 : 1118779312
Total Pages : 389 pages
Book Rating : 4.1/5 (187 download)

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Book Synopsis Harness Oil and Gas Big Data with Analytics by : Keith R. Holdaway

Download or read book Harness Oil and Gas Big Data with Analytics written by Keith R. Holdaway and published by John Wiley & Sons. This book was released on 2014-05-27 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.

Data Analytics in Reservoir Engineering

Author : Sathish Sankaran
Publisher :
ISBN 13 : 9781613998205
Total Pages : 108 pages
Book Rating : 4.9/5 (982 download)

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Book Synopsis Data Analytics in Reservoir Engineering by : Sathish Sankaran

Download or read book Data Analytics in Reservoir Engineering written by Sathish Sankaran and published by . This book was released on 2020-10-29 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analytics in Reservoir Engineering describes the relevance of data analytics for the oil and gas industry, with particular emphasis on reservoir engineering.

Machine Learning and Data Science in the Oil and Gas Industry

Author : Patrick Bangert
Publisher : Gulf Professional Publishing
ISBN 13 : 0128209143
Total Pages : 290 pages
Book Rating : 4.1/5 (282 download)

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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)

Applied Statistical Modeling and Data Analytics

Author : Srikanta Mishra
Publisher : Elsevier
ISBN 13 : 0128032804
Total Pages : 250 pages
Book Rating : 4.1/5 (28 download)

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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

Machine Learning and Data Science in the Oil and Gas Industry

Author : Patrick Bangert
Publisher : Elsevier
ISBN 13 : 0128207140
Total Pages : 288 pages
Book Rating : 4.1/5 (282 download)

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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 Elsevier. This book was released on 2021-03-08 with total page 288 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)

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models

Author : Keith R. Holdaway
Publisher : John Wiley & Sons
ISBN 13 : 1119302587
Total Pages : 368 pages
Book Rating : 4.1/5 (193 download)

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Book Synopsis Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models by : Keith R. Holdaway

Download or read book Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models written by Keith R. Holdaway and published by John Wiley & Sons. This book was released on 2017-10-04 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Harness Oil and Gas Big Data with Analytics

Author : Keith R. Holdaway
Publisher : John Wiley & Sons
ISBN 13 : 1118910893
Total Pages : 389 pages
Book Rating : 4.1/5 (189 download)

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Book Synopsis Harness Oil and Gas Big Data with Analytics by : Keith R. Holdaway

Download or read book Harness Oil and Gas Big Data with Analytics written by Keith R. Holdaway and published by John Wiley & Sons. This book was released on 2014-05-05 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.

Data-Driven Analytics for the Geological Storage of CO2

Author : Shahab Mohaghegh
Publisher : CRC Press
ISBN 13 : 1315280795
Total Pages : 317 pages
Book Rating : 4.3/5 (152 download)

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Book Synopsis Data-Driven Analytics for the Geological Storage of CO2 by : Shahab Mohaghegh

Download or read book Data-Driven Analytics for the Geological Storage of CO2 written by Shahab Mohaghegh and published by CRC Press. This book was released on 2018-05-20 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven analytics is enjoying unprecedented popularity among oil and gas professionals. Many reservoir engineering problems associated with geological storage of CO2 require the development of numerical reservoir simulation models. This book is the first to examine the contribution of artificial intelligence and machine learning in data-driven analytics of fluid flow in porous environments, including saline aquifers and depleted gas and oil reservoirs. Drawing from actual case studies, this book demonstrates how smart proxy models can be developed for complex numerical reservoir simulation models. Smart proxy incorporates pattern recognition capabilities of artificial intelligence and machine learning to build smart models that learn the intricacies of physical, mechanical and chemical interactions using precise numerical simulations. This ground breaking technology makes it possible and practical to use high fidelity, complex numerical reservoir simulation models in the design, analysis and optimization of carbon storage in geological formations projects.

Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems

Author : Mohammed A. Al-Sharafi
Publisher : Springer Nature
ISBN 13 : 3031204298
Total Pages : 703 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems by : Mohammed A. Al-Sharafi

Download or read book Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems written by Mohammed A. Al-Sharafi and published by Springer Nature. This book was released on 2022-12-12 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book sheds light on the recent research directions in intelligent systems and their applications. It involves four main themes: artificial intelligence and data science, recent trends in software engineering, emerging technologies in education, and intelligent health informatics. The discussion of the most recent designs, advancements, and modifications of intelligent systems, as well as their applications, is a key component of the chapters contributed to the aforementioned subjects.

Practical Data Science with Hadoop and Spark

Author : Ofer Mendelevitch
Publisher : Addison-Wesley Professional
ISBN 13 : 0134029720
Total Pages : 463 pages
Book Rating : 4.1/5 (34 download)

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Book Synopsis Practical Data Science with Hadoop and Spark by : Ofer Mendelevitch

Download or read book Practical Data Science with Hadoop and Spark written by Ofer Mendelevitch and published by Addison-Wesley Professional. This book was released on 2016-12-08 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language

Machine Learning in the Oil and Gas Industry

Author : Yogendra Narayan Pandey
Publisher : Apress
ISBN 13 : 9781484260937
Total Pages : 300 pages
Book Rating : 4.2/5 (69 download)

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Book Synopsis Machine Learning in the Oil and Gas Industry by : Yogendra Narayan Pandey

Download or read book Machine Learning in the Oil and Gas Industry written by Yogendra Narayan Pandey and published by Apress. This book was released on 2020-11-03 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Applied Predictive Modeling

Author : Max Kuhn
Publisher : Springer Science & Business Media
ISBN 13 : 1461468493
Total Pages : 600 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Applied Predictive Modeling by : Max Kuhn

Download or read book Applied Predictive Modeling written by Max Kuhn and published by Springer Science & Business Media. This book was released on 2013-05-17 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Business Analytics

Author : Dr. K. Soundararajan
Publisher : Thakur Publication Private Limited
ISBN 13 : 9354804470
Total Pages : 232 pages
Book Rating : 4.3/5 (548 download)

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Book Synopsis Business Analytics by : Dr. K. Soundararajan

Download or read book Business Analytics written by Dr. K. Soundararajan and published by Thakur Publication Private Limited. This book was released on 2022-03-03 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Buy E-Book of Business Analytics Book For MBA 2nd Semester of Anna University, Chennai

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models

Author : Keith R. Holdaway
Publisher : John Wiley & Sons
ISBN 13 : 1119215102
Total Pages : 368 pages
Book Rating : 4.1/5 (192 download)

DOWNLOAD NOW!

Book Synopsis Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models by : Keith R. Holdaway

Download or read book Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models written by Keith R. Holdaway and published by John Wiley & Sons. This book was released on 2017-10-09 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.