Artificial Intelligent Approaches in Petroleum Geosciences
| Korporativní autor: | |
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| Další autoři: | |
| Shrnutí: | XVIII, 277 p. 176 illus., 166 illus. in color. text |
| Jazyk: | angličtina |
| Vydáno: |
Cham :
Springer International Publishing : Imprint: Springer,
2024.
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| Vydání: | 2nd ed. 2024. |
| Témata: | |
| On-line přístup: | https://doi.org/10.1007/978-3-031-52715-9 |
| Médium: | Elektronický zdroj Kniha |
Obsah:
- 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.