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Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems

Name: Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems
Brand: Elsevier Science
SKU: 9780443265105
Price: 60780 HUF
Availability: InStock

Prediction Models Exploiting Well-Log Information

Szerző: David Wood

Nyelv:

Kötés: Puha kötésű

Kiadó: Elsevier Science

Elérhetőség: Beszállítói készleten alacsony példányszámban

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Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Prob...

Információk a könyvről

Szerző

David Wood

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2025

oldal

475

EAN

9780443265105

ISBN

0443265100

Enbook ID

46253559

Kiadó

Elsevier Science

Súly

915

Méretek

191 x 235

Teljes leírás

Implementation and Interpretation of Machine and Deep Learning to Applied Subsurface Geological Problems: Prediction Models Exploiting Well-Log Information explores the implementation of machine and deep learning models to a range of subsurface geological prediction problems commonly encountered in applied resource evaluation and reservoir characterization tasks. It provides readers with insight into how the performance of ML/DL models can be optimized, and sparse datasets of input variables enhanced and/or rescaled, to improve their prediction performances. The author covers a variety of topics in detail, such as 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 several more. Each chapter includes its own introduction, summary, and nomenclature sections together with one or more case studies focused on prediction model implementation related to its topic. The first part of each topic chapter describes the geological issues related to the topic, including an up-to-date literature review. The remainder focuses on prediction modeling of that topic including suitable machine learning and/or deep learning approaches and configurations. Case studies form the latter part of each chapter. Readers in this field will find an invaluable resource to assist them in applying machine and deep learning to their work in sub-surface geoscience.

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
Contrast 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