Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems
Prediction Models Exploiting Well-Log Information
Format:Paperback
Publisher:Elsevier - Health Sciences Division
Publishing:1st Jan '25
£133.99
This title is due to be published on 1st January, and will be despatched as soon as possible.
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.
ISBN: 9780443265105
Dimensions: unknown
Weight: unknown
475 pages