04851nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172072001600188072002300204072001500227245012000242250001800362264007500380300006900455336002600524337002600550338003600576347002400612505127700636520164501913650005203558650002503610650003603635650003703671650003203708650002303740650002403763650003603787650006403823650006103887650002303948700009003971710003404061773002604095776003604121776003604157776003604193856004604229912001404275912001504289999001704304978-3-031-52715-9DE-He21320251030112629.0cr nn 008mamaa240715s2024    sz |    s    |||| 0|eng d  a97830315271599978-3-031-52715-97 a10.1007/978-3-031-52715-92doi 4aTK1041-1078 7aTHF2bicssc 7aTEC0310302bisacsh 7aTHF2thema10aArtificial Intelligent Approaches in Petroleum Geosciencesh[electronic resource] /cedited by Constantin Cranganu.  a2nd ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXVIII, 277 p. 176 illus., 166 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aPreface 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.  aThis 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. 0aCogeneration of electric power and heat.965839 0aFossil fuels.965840 0aArtificial intelligence.958441 0aGeotechnical engineering.966388 0aMathematical models.966910 0aMineralogy.96655214aFossil Fuel.96584224aArtificial Intelligence.95844124aGeotechnical Engineering and Applied Earth Sciences.96638924aMathematical Modeling and Industrial Mathematics.96691224aMineralogy.9665521 aCranganu, Constantin.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9805872 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303152714208iPrinted edition:z978303152716608iPrinted edition:z978303152717340uhttps://doi.org/10.1007/978-3-031-52715-9  aZDB-2-ENE  aZDB-2-SXEN  c61605d61605