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Artificial Intelligent Approaches in Petroleum Geosciences

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Cover of 'Artificial Intelligent Approaches in Petroleum Geosciences'

Table of Contents

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    Book Overview
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    Chapter 1 Intelligent Data Analysis Techniques—Machine Learning and Data Mining
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    Chapter 2 On Meta-heuristics in Optimization and Data Analysis. Application to Geosciences
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    Chapter 3 Genetic Programming Techniques with Applications in the Oil and Gas Industry
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    Chapter 4 Application of Artificial Neural Networks in Geoscience and Petroleum Industry
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    Chapter 5 On Support Vector Regression to Predict Poisson’s Ratio and Young’s Modulus of Reservoir Rock
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    Chapter 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
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    Chapter 7 Active Learning Method for Estimating Missing Logs in Hydrocarbon Reservoirs
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    Chapter 8 Improving the Accuracy of Active Learning Method via Noise Injection for Estimating Hydraulic Flow Units: An Example from a Heterogeneous Carbonate Reservoir
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    Chapter 9 Well Log Analysis by Global Optimization-based Interval Inversion Method
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    Chapter 10 Permeability Estimation in Petroleum Reservoir by Meta-heuristics: An Overview
Attention for Chapter 5: On Support Vector Regression to Predict Poisson’s Ratio and Young’s Modulus of Reservoir Rock
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