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Table of Contents
1 Cover
4 Foreword
5 Preface
7 1 Reservoir Characterization: Fundamental and Applications – An Overview 1.1 Introduction to Reservoir Characterization? 1.2 Data Requirements for Reservoir Characterization 1.3 SURE Challenge 1.4 Reservoir Characterization in the Exploration, Development and Production Phases 1.5 Dynamic Reservoir Characterization (DRC) 1.6 More on Reservoir Characterization and Reservoir Modeling for Reservoir Simulation 1.7 Conclusion 1.8 References
8 Part 2: General Reservoir Characterization and Anomaly Detection
9 2 A Comparison Between Estimated Shear Wave Velocity and Elastic Modulus by Empirical Equations and that of Laboratory Measurements at Reservoir Pressure Condition 2.1 Introduction 2.2 Methodology 2.3 Laboratory Set Up and Measurements 2.4 Results and Discussion 2.5 Conclusions 2.6 Acknowledgment References
10 3 Anomaly Detection within Homogenous Geologic Area 3.1 Introduction 3.2 Anomaly Detection Methodology 3.3 Basic Anomaly Detection Classifiers 3.4 Prior and Posterior Characteristics of Anomaly Detection Performance 3.5 ROC Curve Analysis 3.6 Optimization of Aggregated AD Classifier Using Part of the Anomaly Identified by Universal Classifiers 3.7 Bootstrap Based Tests of Anomaly Type Hypothesis 3.8 Conclusion References
11 4 Characterization of Carbonate Source-Derived Hydrocarbons Using Advanced Geochemical Technologies 4.1 Introduction 4.2 Samples and Analyses Performed 4.3 Results and Discussions 4.4 Summary and Conclusions References
12 5 Strategies in High-Data-Rate MWD Mud Pulse Telemetry 5.1 Summary 5.2 New Technology Elements 5.3 Directional Wave Filtering 5.4 Conclusions Acknowledgments References
13 6 Detection of Geologic Anomalies with Monte Carlo Clustering Assemblies 6.1 Introduction 6.2 Analysis of Inhomogeneity of the Training and Test Sets and Instability of Clustering 6.3 Formation of Multiple Randomized Test Sets and Construction of the Clustering Assemblies 6.4 Irregularity Index of Individual Clusters in the Cluster Set 6.5 Anomaly Indexes of Individual Records and Clustering Assemblies 6.6 Prior and Posterior True and False Discovery Rates for Anomalous and Regular Records 6.7 Estimates of Prior False Discovery Rates for Anomalous Cluster Sets, Clusters, and Individual Records. Permeability Dataset 6.8 Posterior Analysis of Efficiency of Anomaly Identification. High Permeability Anomaly 6.9 Identification of Records in the Gas Sand Dataset as Anomalous, using Brine Sand Dataset as Data with Regular Records 6.10 Notations 6.11 Conclusions References
