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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