Machine Learning for Subsurface Characterization

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  • Publisher : Gulf Professional Publishing
  • Release : 12 October 2019
  • ISBN : 9780128177372
  • Page : 440 pages
  • Rating : 4.5/5 from 103 voters

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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

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Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization
  • Author : Siddharth Misra,Hao Li,Jiabo He
  • Publisher : Gulf Professional Publishing
  • Release Date : 2019-10-12
  • ISBN : 9780128177372
GET THIS BOOKMachine Learning for Subsurface Characterization

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

Machine Learning for the Subsurface Characterization at Core, Well, and Reservoir Scales

Machine Learning for the Subsurface Characterization at Core, Well, and Reservoir Scales
  • Author : Hao Li
  • Publisher : Unknown
  • Release Date : 2020
  • ISBN : OCLC:1152200629
GET THIS BOOKMachine Learning for the Subsurface Characterization at Core, Well, and Reservoir Scales

A Primer on Machine Learning in Subsurface Geosciences

A Primer on Machine Learning in Subsurface Geosciences
  • Author : Shuvajit Bhattacharya
  • Publisher : Springer Nature
  • Release Date : 2021-05-03
  • ISBN : 9783030717681
GET THIS BOOKA Primer on Machine Learning in Subsurface Geosciences

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

Advances in Subsurface Data Analytics

Advances in Subsurface Data Analytics
  • Author : Shuvajit Bhattacharya,Haibin Di
  • Publisher : Elsevier
  • Release Date : 2022-05-27
  • ISBN : 9780128223086
GET THIS BOOKAdvances in Subsurface Data Analytics

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

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization
  • Author : Siddharth Misra,Yifu Han,Yuteng Jin,Pratiksha Tathed
  • Publisher : Elsevier
  • Release Date : 2021-07-13
  • ISBN : 9780128214558
GET THIS BOOKMultifrequency Electromagnetic Data Interpretation for Subsurface Characterization

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization focuses on the development and application of electromagnetic measurement methodologies and their interpretation techniques for subsurface characterization. The book guides readers on how to characterize and understand materials using electromagnetic measurements, including dielectric permittivity, resistivity and conductivity measurements. This reference will be useful for subsurface engineers, petrophysicists, subsurface data analysts, geophysicists, hydrogeologists, and geoscientists who want to know how to develop tools and techniques of electromagnetic measurements and interpretation for subsurface characterization. Includes

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs
  • Author : Koenraad F. Beckers
  • Publisher : Unknown
  • Release Date : 2021
  • ISBN : OCLC:1273558289
GET THIS BOOKSubsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Applications of Artificial Intelligence in Process Systems Engineering

Applications of Artificial Intelligence in Process Systems Engineering
  • Author : Jingzheng Ren,Weifeng Shen,Yi Man,Lichun DOng
  • Publisher : Elsevier
  • Release Date : 2021-06-05
  • ISBN : 9780128217436
GET THIS BOOKApplications of Artificial Intelligence in Process Systems Engineering

Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging

Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering

Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering
  • Author : Goncalo Marques,Joshua O. Ighalo
  • Publisher : Academic Press
  • Release Date : 2022-03-20
  • ISBN : 9780323855983
GET THIS BOOKCurrent Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering

Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering merges computer engineering and environmental engineering. The book presents the latest finding on how data science and AI-based tools are being applied in environmental engineering research. This application involves multiple domains such as data science and artificial intelligence to transform the data collected by intelligent sensors into relevant and reliable information to support decision-making. These tools include fuzzy logic, knowledge-based systems, particle swarm optimization, genetic algorithms, Monte Carlo simulation, artificial

CO2 Injection in the Network of Carbonate Fractures

CO2 Injection in the Network of Carbonate Fractures
  • Author : J. Carlos de Dios,Srikanta Mishra,Flavio Poletto,Alberto Ramos
  • Publisher : Springer Nature
  • Release Date : 2020-12-17
  • ISBN : 9783030629861
GET THIS BOOKCO2 Injection in the Network of Carbonate Fractures

This book presents guidelines for the design, operation and monitoring of CO2 injection in fractured carbonates, with low permeability in the rock matrix, for geological storage in permanent trapping. CO2 migration is dominated by fractures in formations where the hydrodynamic and geochemical effects induced by the injection play a key role influencing the reservoir behavior. CO2 injection in these rocks shows specific characteristics that are different to injection in porous media, as the results from several research studies worldwide reveal.

Advanced Network Technologies and Intelligent Computing

Advanced Network Technologies and Intelligent Computing
  • Author : Isaac Woungang
  • Publisher : Springer Nature
  • Release Date : 2022-06-29
  • ISBN : 9783030960407
GET THIS BOOKAdvanced Network Technologies and Intelligent Computing

Collaborative Computing: Networking, Applications and Worksharing

Collaborative Computing: Networking, Applications and Worksharing
  • Author : Honghao Gao,Xinheng Wang,Muddesar Iqbal,Yuyu Yin,Jianwei Yin,Ning Gu
  • Publisher : Springer Nature
  • Release Date : 2021-01-21
  • ISBN : 9783030675400
GET THIS BOOKCollaborative Computing: Networking, Applications and Worksharing

This two-volume set constitutes the refereed proceedings of the 16th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020, held in Shanghai, China, in October 2020. The 61 full papers and 16 short papers presented were carefully reviewed and selected from 211 submissions. The papers reflect the conference sessions as follows: Collaborative Applications for Network and E-Commerce; Optimization for Collaborate System; Cloud and Edge Computing; Artificial Intelligence; AI Application and Optimization; Classification and Recommendation; Internet of Things; Collaborative Robotics and Autonomous Systems; Smart

Geothermal Energy

Geothermal Energy
  • Author : Kriti Yadav,Anirbid Sircar,Apurwa Yadav
  • Publisher : CRC Press
  • Release Date : 2022-03-23
  • ISBN : 9781000553413
GET THIS BOOKGeothermal Energy

This book focuses on the usage of geothermal energy in countries with low-enthalpy reservoirs. It begins with the fundamentals of geothermal energy and classification of geothermal resources and their importance, including enhanced geothermal systems (EGS). Further, it discusses the creation, production, potential assessment, perspective analysis, life cycle, and environmental assessments of EGS. It describes applications in the field of geothermal energy with relevant case studies and introduces the application of machine learning techniques in the field of geothermal sectors. Features:

Sustainable Geoscience for Natural Gas SubSurface Systems

Sustainable Geoscience for Natural Gas SubSurface Systems
  • Author : David A. Wood,Jianchao Cai
  • Publisher : Gulf Professional Publishing
  • Release Date : 2021-10-30
  • ISBN : 9780323854665
GET THIS BOOKSustainable Geoscience for Natural Gas SubSurface Systems

Sustainable Geoscience for Natural Gas SubSurface Systems delivers many of the scientific fundamentals needed in the natural gas industry, including coal-seam gas reservoir characterization and fracture analysis modeling for shale and tight gas reservoirs. Advanced research includes machine learning applications for well log and facies analysis, 3D gas property geological modeling, and X-ray CT scanning to reduce environmental hazards. Supported by corporate and academic contributors, along with two well-distinguished editors, the book gives today’s natural gas engineers both fundamentals

Machine Learning for Spatial Environmental Data

Machine Learning for Spatial Environmental Data
  • Author : Mikhail Kanevski,Alexei Pozdnoukhov,Alexi Pozdnukhov,Vadim Timonin
  • Publisher : EPFL Press
  • Release Date : 2009-06-09
  • ISBN : 0849382378
GET THIS BOOKMachine Learning for Spatial Environmental Data

Accompanying CD-RM contains Machine learning office software, MLO guide (pdf) and examples of data.

Reservoir Characterization

Reservoir Characterization
  • Author : Fred Aminzadeh
  • Publisher : John Wiley & Sons
  • Release Date : 2022-01-06
  • ISBN : 9781119556213
GET THIS BOOKReservoir Characterization

RESERVOIR CHARACTERIZATION FUNDAMENTALS AND APPLICATIONS The second volume in the series, “Sustainable Energy Engineering,” written by some of the foremost authorities in the world on reservoir engineering, this groundbreaking new volume presents the most comprehensive and updated new processes, equipment, and practical applications in the field. Long thought of as not being “sustainable,” newly discovered sources of petroleum and newly developed methods for petroleum extraction have made it clear that not only can the petroleum industry march toward sustainability, but