Artificial Intelligent Approaches in Petroleum Geosciences

Herausgegeben:Cranganu, Constantin

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-52714-2
• 2. Aufl.
• Seitenzahl: 296
• Erscheinungstermin: 16. Juli 2024
• Englisch
• Abmessung: 241mm x 160mm x 21mm
• Gewicht: 609g
• ISBN-13: 9783031527142
• ISBN-10: 3031527143
• Artikelnr.: 69652777

Autorenporträt

Constantin Cranganu is a professor of geophysics and petroleum geology at Brooklyn College of the City University of New York. He obtained a Ph.D. degree (ABD) from the University of Bucharest, Romania (1993), in geophysics and another Ph.D. from the University of Oklahoma (1997) in geology.  Before coming to Brooklyn College, he worked at "Al. I. Cuza" University of Iasi, Romania, and the School of Geology and Geophysics of University of Oklahoma. His main research covers various areas of petroleum geosciences: oil and gas generation, abnormal fluid pressures in sedimentary basins, gas hydrate exploitation, identification of gas-bearing layers using well logs, geostatistics, etc. Lately, Prof. Cranganu started using artificial intelligent approaches in his petroleum-related research. He published many books, peer-reviewed articles, book reviews, and essays. His paper, "Using gene expression programming to estimate sonic log distributions based on the natural gamma ray and deep resistivity logs: A case study from the Anadarko Basin, Oklahoma", (co-author Elena Bautu), published in Journal of Petroleum Science and Engineering in 2012 was nominated for ENI Awards 2012. In 2014, he was the author and the senior editor of "Artificial Intelligent Approaches in Petroleum Geosciences", Springer, 1st edition.

Inhaltsangabe

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.

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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.