05031nam a22005775i 4500 978-3-031-52715-9 DE-He213 20251030112629.0 cr nn 008mamaa 240715s2024 sz | s |||| 0|eng d 9783031527159 978-3-031-52715-9 10.1007/978-3-031-52715-9 doi TK1041-1078 THF bicssc TEC031030 bisacsh THF thema Artificial Intelligent Approaches in Petroleum Geosciences [electronic resource] / edited by Constantin Cranganu. 2nd ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XVIII, 277 p. 176 illus., 166 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. Cogeneration of electric power and heat. 65839 Fossil fuels. 65840 Artificial intelligence. 58441 Geotechnical engineering. 66388 Mathematical models. 66910 Mineralogy. 66552 Fossil Fuel. 65842 Artificial Intelligence. 58441 Geotechnical Engineering and Applied Earth Sciences. 66389 Mathematical Modeling and Industrial Mathematics. 66912 Mineralogy. 66552 Cranganu, Constantin. editor. edt http://id.loc.gov/vocabulary/relators/edt 80587 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031527142 Printed edition: 9783031527166 Printed edition: 9783031527173 https://doi.org/10.1007/978-3-031-52715-9 ZDB-2-ENE ZDB-2-SXEN 61605 61605