Machine 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 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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  • Author : Siddharth Misra
  • Publisher : Gulf Professional Publishing
  • Pages : 440 pages
  • ISBN : 0128177373
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMachine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization

Machine Learning for Subsurface Characterization
  • Author : Siddharth Misra,Hao Li,Jiabo He
  • Publisher : Gulf Professional Publishing
  • Release : 12 October 2019
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

A Primer on Machine Learning in Subsurface Geosciences

A Primer on Machine Learning in Subsurface Geosciences
  • Author : Shuvajit Bhattacharya
  • Publisher : Springer Nature
  • Release : 03 May 2021
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

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 : 13 July 2021
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

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 : 05 June 2021
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

Machine Learning Guide for Oil and Gas Using Python

Machine Learning Guide for Oil and Gas Using Python
  • Author : Hoss Belyadi,Alireza Haghighat
  • Publisher : Gulf Professional Publishing
  • Release : 09 April 2021
GET THIS BOOKMachine Learning Guide for Oil and Gas Using Python

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

Advances in Subsurface Data Analytics

Advances in Subsurface Data Analytics
  • Author : Shuvajit Bhattacharya,Haibin Di
  • Publisher : Elsevier
  • Release : 15 March 2022
GET THIS BOOKAdvances in Subsurface Data Analytics

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

Machine Learning in the Oil and Gas Industry

Machine Learning in the Oil and Gas Industry
  • Author : Yogendra Narayan Pandey,Ayush Rastogi,Sribharath Kainkaryam,Srimoyee Bhattacharya,Luigi Saputelli
  • Publisher : Apress
  • Release : 03 November 2020
GET THIS BOOKMachine Learning in the Oil and Gas Industry

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

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
  • Author : Anonim
  • Publisher : Academic Press
  • Release : 25 September 2020
GET THIS BOOKMachine Learning and Artificial Intelligence in Geosciences

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well

Earth Observation Open Science and Innovation

Earth Observation Open Science and Innovation
  • Author : Pierre-Philippe Mathieu,Christoph Aubrecht
  • Publisher : Springer
  • Release : 23 January 2018
GET THIS BOOKEarth Observation Open Science and Innovation

This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well

geoENV VI – Geostatistics for Environmental Applications

geoENV VI – Geostatistics for Environmental Applications
  • Author : Amílcar Soares,Maria João Pereira,Roussos Dimitrakopoulos
  • Publisher : Springer Science & Business Media
  • Release : 12 March 2008
GET THIS BOOKgeoENV VI – Geostatistics for Environmental Applications

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.