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Artificial intelligent approaches in petroleum geosciences

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligent approaches in petroleum geosciencesedited by Constantin Cranganu.
other author:	Cranganu, Constantin.
Published:	Cham :Springer International Publishing :2024.
Description:	xviii, 277 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	PetroleumGeology
Online resource:	https://doi.org/10.1007/978-3-031-52715-9
ISBN:	9783031527159$q(electronic bk.)

Artificial intelligent approaches in petroleum geosciences
Artificial intelligent approaches in petroleum geosciences[electronic resource] /edited by Constantin Cranganu. - Second edition. - Cham :Springer International Publishing :2024. - xviii, 277 p. :ill. (chiefly col.), digital ;24 cm.

Preface to the 2nd edition -- Preface to the 1st Edition -- 1. Applications of Data-Driven Techniques in Reservoir Modeling and Management -- Part 1: Waterflooding -- Part 2: Water Alternating Gas Injection, CO2 Storage, and Property Estimations -- 2. Comparison of three machine learning approaches in determining Total Organic Carbon (TOC): A case study from Marcellus shale formation, New York state -- 3. Gated Recurrent Units for Lithofacies Classification based on Seismic Inversion -- 4. Application of Artificial Neural Networks in Geoscience and Petroleum Industry -- 5. On Support Vector Regression to Predict Poisson's Ratio and Young's Modulus of Reservoir Rock -- 6. Use of Active Learning Method to Determine the Presence and Estimate the Magnitude of Abnormally Pressured Fluid Zones: A Case Study from the Anadarko Basin, Oklahoma -- 7. Active Learning Method for Estimating Missing Logs in Hydrocarbon Reservoirs -- 8. Improving the Accuracy of Active Learning Method via Noise Injection for Estimating Hydraulic Flow Units: An Example from a Heterogeneous Carbonate Reservoir -- 9. Well Log Analysis by Global Optimization-based Interval Inversion Method -- 10. Permeability Estimation in Petroleum Reservoir by Meta-heuristics: An Overview -- Index.

This book presents cutting-edge approaches to solving practical problems faced by professionals in the petroleum industry and geosciences. With various state-of-the-art working examples from experienced academics, the book offers an exposure to the latest developments in intelligent methods for oil and gas research, exploration, and production. This second edition is updated with new chapters on machine learning approaches, data-driven modelling techniques, and neural networks. The book delves into machine learning approaches, including evolutionary algorithms, swarm intelligence, fuzzy logic, deep artificial neural networks, KNN, decision tree, random forest, XGBoost, and LightGBM. it also analyzes the strengths and weaknesses of each method and emphasizes essential parameters like robustness, accuracy, speed of convergence, computer time, overlearning, and normalization. Integration, data handling, risk management, and uncertainty management are all crucial issues in petroleum geosciences. The complexities of these problems require a multidisciplinary approach that fuses petroleum engineering, geology, geophysics, and geochemistry. Essentially, this book presents an approach for integrating various disciplines such as data fusion, risk reduction, and uncertainty management. Whether you are a professional or a student, you can greatly benefit from the latest advancements in intelligent methods applied to oil and gas research. This comprehensive and updated book presents cutting-edge approaches and real-world examples that can help you in solving the intricate challenges of the petroleum industry and geosciences.

ISBN: 9783031527159$q(electronic bk.)

Standard No.: 10.1007/978-3-031-52715-9doiSubjects--Topical Terms:

666640
Petroleum
--Geology

LC Class. No.: TN870.5

Dewey Class. No.: 662.6028563

Artificial intelligent approaches in petroleum geosciences
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245 0 0 $a Artificial intelligent approaches in petroleum geosciences $h [electronic resource] / $c edited by Constantin Cranganu.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xviii, 277 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a Preface to the 2nd edition -- Preface to the 1st Edition -- 1. Applications of Data-Driven Techniques in Reservoir Modeling and Management -- Part 1: Waterflooding -- Part 2: Water Alternating Gas Injection, CO2 Storage, and Property Estimations -- 2. Comparison of three machine learning approaches in determining Total Organic Carbon (TOC): A case study from Marcellus shale formation, New York state -- 3. Gated Recurrent Units for Lithofacies Classification based on Seismic Inversion -- 4. Application of Artificial Neural Networks in Geoscience and Petroleum Industry -- 5. On Support Vector Regression to Predict Poisson's Ratio and Young's Modulus of Reservoir Rock -- 6. Use of Active Learning Method to Determine the Presence and Estimate the Magnitude of Abnormally Pressured Fluid Zones: A Case Study from the Anadarko Basin, Oklahoma -- 7. Active Learning Method for Estimating Missing Logs in Hydrocarbon Reservoirs -- 8. Improving the Accuracy of Active Learning Method via Noise Injection for Estimating Hydraulic Flow Units: An Example from a Heterogeneous Carbonate Reservoir -- 9. Well Log Analysis by Global Optimization-based Interval Inversion Method -- 10. Permeability Estimation in Petroleum Reservoir by Meta-heuristics: An Overview -- Index.
520 $a This book presents cutting-edge approaches to solving practical problems faced by professionals in the petroleum industry and geosciences. With various state-of-the-art working examples from experienced academics, the book offers an exposure to the latest developments in intelligent methods for oil and gas research, exploration, and production. This second edition is updated with new chapters on machine learning approaches, data-driven modelling techniques, and neural networks. The book delves into machine learning approaches, including evolutionary algorithms, swarm intelligence, fuzzy logic, deep artificial neural networks, KNN, decision tree, random forest, XGBoost, and LightGBM. it also analyzes the strengths and weaknesses of each method and emphasizes essential parameters like robustness, accuracy, speed of convergence, computer time, overlearning, and normalization. Integration, data handling, risk management, and uncertainty management are all crucial issues in petroleum geosciences. The complexities of these problems require a multidisciplinary approach that fuses petroleum engineering, geology, geophysics, and geochemistry. Essentially, this book presents an approach for integrating various disciplines such as data fusion, risk reduction, and uncertainty management. Whether you are a professional or a student, you can greatly benefit from the latest advancements in intelligent methods applied to oil and gas research. This comprehensive and updated book presents cutting-edge approaches and real-world examples that can help you in solving the intricate challenges of the petroleum industry and geosciences.
650 0 $a Petroleum $x Geology $x Data processing. $3 666640
650 0 $a Artificial intelligence $x Geophysical applications. $3 719469
650 0 $a Artificial intelligence. $3 194058
650 1 4 $a Fossil Fuel. $3 913237
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Geotechnical Engineering and Applied Earth Sciences. $3 913450
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 274070
650 2 4 $a Mineralogy. $3 273728
700 1 $a Cranganu, Constantin. $3 719466
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-52715-9
950 $a Energy (SpringerNature-40367)