Download Machine Learning for Subsurface Characterization Ebook PDF

Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization
A Book

by Siddharth Misra,Hao Li,Jiabo He

  • Publisher : Gulf Professional Publishing
  • Release : 2019-10-12
  • Pages : 440
  • ISBN : 0128177373
  • Language : En, Es, Fr & De
GET BOOK

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

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

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

by Hao Li

  • Publisher : Unknown Publisher
  • Release : 2020
  • Pages : 228
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

A Primer on Machine Learning in Subsurface Geosciences

A Primer on Machine Learning in Subsurface Geosciences
A Book

by Shuvajit Bhattacharya

  • Publisher : Springer Nature
  • Release : 2021-05-03
  • Pages : 172
  • ISBN : 3030717682
  • Language : En, Es, Fr & De
GET BOOK

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.

Advances in Subsurface Data Analytics

Advances in Subsurface Data Analytics
Traditional and Physics-Based Machine Learning

by Shuvajit Bhattacharya,Haibin Di

  • Publisher : Elsevier
  • Release : 2022-03-01
  • Pages : 400
  • ISBN : 0128223081
  • Language : En, Es, Fr & De
GET BOOK

Advances in Subsurface Data Analytics: Traditional and Physics-Based Machine Learning brings together popular, emerging machine learning algorithms and their applications in subsurface analysis, including geology, geophysics and petrophysics. Each chapter focuses on one machine learning algorithm and includes detailed workflow, applications and case studies. In addition, some of the chapters contain algorithm comparisons to better equip readers with different strategies to implement automated workflows for subsurface analysis. This book will help researchers in academia and professional geoscientists working in the oil and gas industry understand and appreciate the existence of machine learning and deep learning models. In addition, users will learn how to optimize performance and explore applications in the geosciences by bringing together several contributions in a single volume. Covers the fundamentals of simple machine learning and emerging deep learning algorithms written by practitioners in academia and industry Presents detailed case studies of individual machine learning algorithms around the world, including those used for conventional and unconventional reservoirs Offers an analysis of future trends

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs
Preprint

by Koenraad F. Beckers

  • Publisher : Unknown Publisher
  • Release : 2021
  • Pages : 8
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization
A Book

by Siddharth Misra,Yifu Han,Yuteng Jin,Pratiksha Tathed

  • Publisher : Elsevier
  • Release : 2021-07-13
  • Pages : 382
  • ISBN : 0128214554
  • Language : En, Es, Fr & De
GET BOOK

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 case studies to add additional color to the presented content Provides codes for the mechanistic modeling of multi-frequency conductivity and relative permittivity of porous geomaterials Presents detailed descriptions of multifrequency electromagnetic data interpretation models and inversion algorithm

Applications of Artificial Intelligence in Process Systems Engineering

Applications of Artificial Intelligence in Process Systems Engineering
A Book

by Jingzheng Ren,Weifeng Shen,Yi Man,Lichun DOng

  • Publisher : Elsevier
  • Release : 2021-06-05
  • Pages : 540
  • ISBN : 012821743X
  • Language : En, Es, Fr & De
GET BOOK

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 areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering

CO2 Injection in the Network of Carbonate Fractures

CO2 Injection in the Network of Carbonate Fractures
A Book

by J. Carlos de Dios,Srikanta Mishra,Flavio Poletto,Alberto Ramos

  • Publisher : Springer Nature
  • Release : 2020-12-17
  • Pages : 243
  • ISBN : 3030629864
  • Language : En, Es, Fr & De
GET BOOK

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. All aspects of a project of this type are discussed in this text, from the drilling to the injection, as well as support works like well logging, laboratory and field tests, modeling, and risk assessment. Examples are provided, lesson learned is detailed, and conclusions are drawn. This work is derived from the experience of international research teams and particularly from that gained during the design, construction and operation of Hontomín Technology Development Plant. Hontomín research pilot is currently the only active onshore injection site in the European Union, operated by Fundación Ciudad de la Energía-CIUDEN F.S.P. and recognized by the European Parliament as a key test facility. The authors provide guidelines and tools to enable readers to find solutions to their problems. The book covers activities relevant to a wide range of practitioners involved in reservoir exploration, modeling, site operation and monitoring. Fluid injection in fractured media shows specific features that are different than injection in porous media, influencing the reservoir behavior and defining conditions for safe and efficient operation. Therefore, this book is also useful to professionals working on oil & gas, hydrogeology and geothermal projects, and in general for those whose work is related to activities using fluid injection in the ground.

Collaborative Computing: Networking, Applications and Worksharing

Collaborative Computing: Networking, Applications and Worksharing
16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16–18, 2020, Proceedings, Part II

by Honghao Gao,Xinheng Wang,Muddesar Iqbal,Yuyu Yin,Jianwei Yin,Ning Gu

  • Publisher : Springer Nature
  • Release : 2021-01-21
  • Pages : 578
  • ISBN : 3030675408
  • Language : En, Es, Fr & De
GET BOOK

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 Transportation.

Geothermal Energy

Geothermal Energy
Utilization, Technology and Financing

by Kriti Yadav,Anirbid Sircar,Apurwa Yadav

  • Publisher : CRC Press
  • Release : 2022-03-23
  • Pages : 174
  • ISBN : 1000553418
  • Language : En, Es, Fr & De
GET BOOK

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: Focuses on the development of low- to moderate-enthalpy geothermal resources Introduces machine learning tools and artificial intelligence as applied to geothermal energy Provides an understanding of geothermal energy resources and EGS Discusses the possibility of EGS using spallation and laser drilling Includes stimulation methods (thermal, hydraulic, chemical, and explosive) and case studies This book is aimed at researchers and graduate students in geology, clean energy, geothermal energy, and thermal engineering.

Machine Learning for Spatial Environmental Data

Machine Learning for Spatial Environmental Data
Theory, Applications, and Software

by Mikhail Kanevski,Alexei Pozdnoukhov,Alexi Pozdnukhov,Vadim Timonin

  • Publisher : EPFL Press
  • Release : 2009-06-09
  • Pages : 377
  • ISBN : 9780849382376
  • Language : En, Es, Fr & De
GET BOOK

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

geoENV VI – Geostatistics for Environmental Applications

geoENV VI – Geostatistics for Environmental Applications
A Book

by Amílcar Soares,Maria João Pereira,Roussos Dimitrakopoulos

  • Publisher : Springer Science & Business Media
  • Release : 2008-03-12
  • Pages : 511
  • ISBN : 1402064489
  • Language : En, Es, Fr & De
GET BOOK

This volume contains 40 selected full-text contributions from the Sixth European Conference on Geostatistics for Environmental Applications, geoENV IV, held in Rhodes, Greece, October 25-26, 2006. The objective of the editors was to compile a set of papers from which the reader could perceive how geostatistics is applied within the environmental sciences. A few selected theoretical contributions are also included.

The Development of Comparative Information Yield Curves for Application to Subsurface Characterization

The Development of Comparative Information Yield Curves for Application to Subsurface Characterization
A Book

by Felipe Pereira Jorge De Barros

  • Publisher : Unknown Publisher
  • Release : 2009
  • Pages : 338
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Characterization, Modeling, Monitoring, and Remediation of Fractured Rock

Characterization, Modeling, Monitoring, and Remediation of Fractured Rock
A Book

by National Academies of Sciences, Engineering, and Medicine,Division on Earth and Life Studies,Board on Earth Sciences and Resources,Committee on Geological and Geotechnical Engineering,Committee on Subsurface Characterization, Modeling, Monitoring, and Remediation of Fractured Rock

  • Publisher : National Academies Press
  • Release : 2021-01-29
  • Pages : 176
  • ISBN : 0309373727
  • Language : En, Es, Fr & De
GET BOOK

Fractured rock is the host or foundation for innumerable engineered structures related to energy, water, waste, and transportation. Characterizing, modeling, and monitoring fractured rock sites is critical to the functioning of those infrastructure, as well as to optimizing resource recovery and contaminant management. Characterization, Modeling, Monitoring, and Remediation of Fractured Rock examines the state of practice and state of art in the characterization of fractured rock and the chemical and biological processes related to subsurface contaminant fate and transport. This report examines new developments, knowledge, and approaches to engineering at fractured rock sites since the publication of the 1996 National Research Council report Rock Fractures and Fluid Flow: Contemporary Understanding and Fluid Flow. Fundamental understanding of the physical nature of fractured rock has changed little since 1996, but many new characterization tools have been developed, and there is now greater appreciation for the importance of chemical and biological processes that can occur in the fractured rock environment. The findings of Characterization, Modeling, Monitoring, and Remediation of Fractured Rock can be applied to all types of engineered infrastructure, but especially to engineered repositories for buried or stored waste and to fractured rock sites that have been contaminated as a result of past disposal or other practices. The recommendations of this report are intended to help the practitioner, researcher, and decision maker take a more interdisciplinary approach to engineering in the fractured rock environment. This report describes how existing tools-some only recently developed-can be used to increase the accuracy and reliability of engineering design and management given the interacting forces of nature. With an interdisciplinary approach, it is possible to conceptualize and model the fractured rock environment with acceptable levels of uncertainty and reliability, and to design systems that maximize remediation and long-term performance. Better scientific understanding could inform regulations, policies, and implementation guidelines related to infrastructure development and operations. The recommendations for research and applications to enhance practice of this book make it a valuable resource for students and practitioners in this field.

Sustainable Geoscience for Natural Gas SubSurface Systems

Sustainable Geoscience for Natural Gas SubSurface Systems
A Book

by David A. Wood,Jianchao Cai

  • Publisher : Gulf Professional Publishing
  • Release : 2021-10-30
  • Pages : 434
  • ISBN : 0323854664
  • Language : En, Es, Fr & De
GET BOOK

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 and advances in a convenient resource, with a zero-carbon future in mind. Includes structured case studies to illustrate how new principles can be applied in practical situations Helps readers understand advanced topics, including machine learning applications to optimize predictions, controls and improve knowledge-based applications Provides tactics to accelerate emission reductions Teaches gas fracturing mechanics aimed at reducing environmental impacts, along with enhanced oil recovery technologies that capture carbon dioxide

Chemical Export to River Systems from the Critical Zone

Chemical Export to River Systems from the Critical Zone
A Book

by Carl I. Steefel,Alexis Navarre-Sitchler,Pamela L. Sullivan

  • Publisher : Frontiers Media SA
  • Release : 2021-11-30
  • Pages : 129
  • ISBN : 2889717348
  • Language : En, Es, Fr & De
GET BOOK

Stochastic Modeling in Hydrogeology

Stochastic Modeling in Hydrogeology
A Book

by J. Jaime Gómez-Hernández,Liangping Li,Teng Xu,Andrés Alcolea

  • Publisher : Frontiers Media SA
  • Release : 2021-07-14
  • Pages : 129
  • ISBN : 2889710378
  • Language : En, Es, Fr & De
GET BOOK

Dr. Andres Alcolea is employed by Geo-Energie Suisse AG and is the funder and CEO of HydroGeoModels. All other Topic Editors declare no competing interests with regards to the Research Topic subject

Future Directions for the U.S. Geological Survey's Energy Resources Program

Future Directions for the U.S. Geological Survey's Energy Resources Program
A Book

by National Academies of Sciences, Engineering, and Medicine,Division on Earth and Life Studies,Board on Earth Sciences and Resources,Committee on Earth Resources,Committee on Future Directions for the U.S. Geological Survey's Energy Resources Program

  • Publisher : National Academies Press
  • Release : 2018-09-04
  • Pages : 168
  • ISBN : 0309477433
  • Language : En, Es, Fr & De
GET BOOK

Reliable, affordable, and technically recoverable energy is central to the nation's economic and social vitality. The United States is both a major consumer of geologically based energy resources from around the world and - increasingly of late - a developer of its own energy resources. Understanding the national and global availability of those resources as well as the environmental impacts of their development is essential for strategic decision making related to the nation's energy mix. The U.S. Geological Survey Energy Resources Program is charged with providing unbiased and publicly available national- and regional-scale assessments of the location, quantity, and quality of geologically based energy resources and with undertaking research related to their development. At the request of the Energy Resources Program (ERP), this publication considers the nation's geologically based energy resource challenges in the context of current national and international energy outlooks. Future Directions for the U.S. Geological Survey's Energy Resources Program examines how ERP activities and products address those challenges and align with the needs federal and nonfederal consumers of ERP products. This study contains recommendations to develop ERP products over the next 10-15 years that will most effectively inform both USGS energy research priorities and the energy needs and priorities of the U.S. government.

Smart Manufacturing

Smart Manufacturing
Applications and Case Studies

by Masoud Soroush,Michael Baldea,Thomas F. Edgar

  • Publisher : Elsevier
  • Release : 2020-08-04
  • Pages : 528
  • ISBN : 0128203811
  • Language : En, Es, Fr & De
GET BOOK

Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. Smart Manufacturing: Applications and Case Studies includes information about the key applications of these new methods, as well as practitioners’ accounts of real-life applications and case studies. Written by thought leaders in the field from around the world, Smart Manufacturing: Applications and Case Studies is essential reading for graduate students, researchers, process engineers and managers. It is complemented by a companion book titled Smart Manufacturing: Concepts and Methods, which describes smart manufacturing methods in detail. Includes examples of applications of smart manufacturing in process industries Provides a thorough overview of the subject and practical examples of applications through well researched case studies Offers insights and accounts of first-hand experiences to motivate further implementations of the key concepts of smart manufacturing

Study of AI Based Methods for Characterization of Geotechnical Site Investigation Data

Study of AI Based Methods for Characterization of Geotechnical Site Investigation Data
A Book

by Hui Wang,Xiangrong Wang,Robert Y. Liang

  • Publisher : Unknown Publisher
  • Release : 2020
  • Pages : 51
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
GET BOOK

Due to the inadequate knowledge of the soil forming histories and/or human activities, the subsurface soil layers are difficult to ascertain. Subsurface uncertainty and its influence on geotechnical design have long been a challenge facing practitioners. Recently, the ASCE Geo-institute has developed the Data Interchange for Geotechnical and Geoenvironmental Specialists (DIGGS), which is a standard schema for transferring geotechnical data between multiple organizations. It paves the way of sharing and unifying datasets and forms a structural database for further data-driven modeling and analysis. The Office of Geotechnical Engineering at ODOT (OGE) is taking a national leading role in supporting the development efforts of DIGGS and hence make this project possible. In this study, site investigation data in DIGGS format and archived format are jointly processed. An innovative technique developed by the research team has been further improved for better application in real-world projects. Bayesian machine learning is integrated with Markov random field models to infer and simulate subsurface models and geospatial data with quantified uncertainty. Spatial heterogeneity and statistical characteristics are modeled in terms of statistical and spatial patterns. These patterns serve as a basis to provide a synthesized interpretation of the soil profiles with uncertainty quantified. Four (4) validation projects have been performed in this report and the results are well documented. Summary and recommendations for future work are also provided. A short introduction of the key concepts behind this technique, and pathway for converting the existing program into a ready for implementation web-based program for potential ODOT usages are provided in the appendices.