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Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems Prediction Models Exploiting Well-Log Information

Heftet / 2025 / Engelsk

Produktdetaljer

ISBN
9780443265105
Publisert
2025-03-25
Utgiver
Vendor
Elsevier - Health Sciences Division
Vekt
930 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
442

Biografisk notat

David A. Wood has more than forty years of international gas, oil, and broader energy experience since gaining his Ph.D. in geosciences from Imperial College London in the 1970s. His expertise covers multiple fields including subsurface geoscience and engineering relating to oil and gas exploration and production, energy supply chain technologies, and efficiencies. For the past two decades, David has worked as an independent international consultant, researcher, training provider, and expert witness. He has published an extensive body of work on geoscience, engineering, energy, and machine learning topics. He currently consults and conducts research on a variety of technical and commercial aspects of energy and environmental issues through his consultancy, DWA Energy Limited. He has extensive editorial experience as a founding editor of Elsevier’s Journal of Natural Gas Science & Engineering in 2008/9 then serving as Editor-in-Chief from 2013 to 2016. He is currently Co-Editor-in-Chief of Advances in Geo-Energy Research.

Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems Prediction Models Exploiting Well-Log Information

Heftet / 2025 / Engelsk
Heftet / 2025 / Engelsk
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Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores machine and deep learning models for subsurface geological prediction problems commonly encountered in applied resource evaluation and reservoir characterization tasks. The book provides insights into how the performance of ML/DL models can be optimized—and sparse datasets of input variables enhanced and/or rescaled—to improve prediction performances. A variety of topics are covered, including regression models to estimate total organic carbon from well-log data, predicting brittleness indexes in tight formation sequences, trapping mechanisms in potential sub-surface carbon storage reservoirs, and more. Each chapter includes its own introduction, summary, and nomenclature sections, along with one or more case studies focused on prediction model implementation related to its topic.
Les mer
1. Regression models to estimate total organic carbon (TOC) from well-log data 2. Predicting brittleness indexes in tight formation sequences 3. Classifying lithofacies in clastic, carbonate, and mixed reservoir sequences 4. Permeability and water saturation distributions in complex reservoirs 5. Trapping mechanisms in potential sub-surface carbon storage reservoirs 6. The accurate picking of formation tops in field development wells 7. Assessing formation loss of circulation risks with mud-log datasets 8. Delineating fracture densities and apertures using well-log image data 9. Determining reservoir microfacies using photomicrograph and computed tomography image data 10. Characterizing coal-bed methane reservoirs with well-log datasets
Les mer
Covers implementation methods and requirements to apply and interpret machine learning methods for sub-surface geoscience and engineering problems
Addresses common applied geological problems focused on machine and deep learning implementation with case studies Considers regression, classification, and clustering machine learning methods and how to optimize and assess their performance, considering suitable error and accuracy metric Contrasts the pros and cons of multiple machine and deep learning methods Includes techniques to improve the identification of geological carbon capture and storage reservoirs, a key part of many energy transition strategies
Les mer

Relaterte produkter

Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores machine and deep learning models for subsurface geological prediction problems commonly encountered in applied resource evaluation and reservoir characterization tasks. The book provides insights into how the performance of ML/DL models can be optimized—and sparse datasets of input variables enhanced and/or rescaled—to improve prediction performances. A variety of topics are covered, including regression models to estimate total organic carbon from well-log data, predicting brittleness indexes in tight formation sequences, trapping mechanisms in potential sub-surface carbon storage reservoirs, and more. Each chapter includes its own introduction, summary, and nomenclature sections, along with one or more case studies focused on prediction model implementation related to its topic.
Les mer
1. Regression models to estimate total organic carbon (TOC) from well-log data 2. Predicting brittleness indexes in tight formation sequences 3. Classifying lithofacies in clastic, carbonate, and mixed reservoir sequences 4. Permeability and water saturation distributions in complex reservoirs 5. Trapping mechanisms in potential sub-surface carbon storage reservoirs 6. The accurate picking of formation tops in field development wells 7. Assessing formation loss of circulation risks with mud-log datasets 8. Delineating fracture densities and apertures using well-log image data 9. Determining reservoir microfacies using photomicrograph and computed tomography image data 10. Characterizing coal-bed methane reservoirs with well-log datasets
Les mer
Covers implementation methods and requirements to apply and interpret machine learning methods for sub-surface geoscience and engineering problems
Addresses common applied geological problems focused on machine and deep learning implementation with case studies Considers regression, classification, and clustering machine learning methods and how to optimize and assess their performance, considering suitable error and accuracy metric Contrasts the pros and cons of multiple machine and deep learning methods Includes techniques to improve the identification of geological carbon capture and storage reservoirs, a key part of many energy transition strategies
Les mer

Produktdetaljer

ISBN
9780443265105
Publisert
2025-03-25
Utgiver
Vendor
Elsevier - Health Sciences Division
Vekt
930 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
442

Biografisk notat

David A. Wood has more than forty years of international gas, oil, and broader energy experience since gaining his Ph.D. in geosciences from Imperial College London in the 1970s. His expertise covers multiple fields including subsurface geoscience and engineering relating to oil and gas exploration and production, energy supply chain technologies, and efficiencies. For the past two decades, David has worked as an independent international consultant, researcher, training provider, and expert witness. He has published an extensive body of work on geoscience, engineering, energy, and machine learning topics. He currently consults and conducts research on a variety of technical and commercial aspects of energy and environmental issues through his consultancy, DWA Energy Limited. He has extensive editorial experience as a founding editor of Elsevier’s Journal of Natural Gas Science & Engineering in 2008/9 then serving as Editor-in-Chief from 2013 to 2016. He is currently Co-Editor-in-Chief of Advances in Geo-Energy Research.

Relaterte produkter