Foreword xix Preface xxiii Part 1: Introduction 1 1 Reservoir Characterization: Fundamental and Applications - An Overview 3 Fred Aminzadeh 1.1 Introduction to Reservoir Characterization? 3 1.2 Data Requirements for Reservoir Characterization 5 1.3 SURE Challenge 7 1.4 Reservoir Characterization in the Exploration, Development and Production Phases 10 1.4.1 Exploration Stage/Development Stage 10 1.4.

2 Primary Production Stage 11 1.4.3 Secondary/Tertiary Production Stage 11 1.5 Dynamic Reservoir Characterization (DRC) 12 1.5.1 4D Seismic for DRC 13 1.5.2 Microseismic Data for DRC 14 1.

6 More on Reservoir Characterization and Reservoir Modeling for Reservoir Simulation 15 1.6.1 Rock Physics 16 1.6.2 Reservoir Modeling 17 1.7 Conclusion 20 References 20 Part 2: General Reservoir Characterization and Anomaly Detection 23 2 A Comparison Between Estimated Shear Wave Velocity and Elastic Modulus by Empirical Equations and that of Laboratory Measurements at Reservoir Pressure Condition 25 Haleh Azizia, Hamid Reza Siahkoohi, Brian Evans, Nasser Keshavarz Farajkhah and Ezatollah KazemZadeh 2.1 Introduction 26 2.2 Methodology 28 2.

1.2 Estimating the Shear Wave Velocity 28 2.2.2 Estimating Geomechanical Parameters 31 2.3 Laboratory Set Up and Measurements 32 2.3.1 Laboratory Data Collection 34 2.4 Results and Discussion 35 2.

5 Conclusions 41 2.6 Acknowledgment 43 References 43 3 Anomaly Detection within Homogenous Geologic Area 47 Simon Katz, Fred Aminzadeh, George Chilingar and Leonid Khilyuk 3.1 Introduction 48 3.2 Anomaly Detection Methodology 49 3.3 Basic Anomaly Detection Classifiers 50 3.4 Prior and Posterior Characteristics of Anomaly Detection Performance 52 3.5 ROC Curve Analysis 55 3.6 Optimization of Aggregated AD Classifier Using Part of the Anomaly Identified by Universal Classifiers 58 3.

7 Bootstrap Based Tests of Anomaly Type Hypothesis 61 3.8 Conclusion 64 References 65 4 Characterization of Carbonate Source-Derived Hydrocarbons Using Advanced Geochemical Technologies 69 Hossein Alimi 4.1 Introduction 70 4.2 Samples and Analyses Performed 71 4.3 Results and Discussions 72 4.4 Summary and Conclusions 79 References 80 5 Strategies in High-Data-Rate MWD Mud Pulse Telemetry 81 Yinao Su, Limin Sheng, Lin Li, Hailong Bian, Rong Shi, Xiaoying Zhuang and Wilson Chin 5.1 Summary 82 5.1.

1 High Data Rates and Energy Sustainability 82 5.1.2 Introduction 83 5.1.3 MWD Telemetry Basics 85 5.1.4 New Telemetry Approach 87 5.2 New Technology Elements 88 5.

2.1 Downhole Source and Signal Optimization 89 5.2.2 Surface Signal Processing and Noise Removal 92 5.2.3 Pressure, Torque and Erosion Computer Modeling 93 5.2.4 Wind Tunnel Analysis: Studying New Approaches 96 5.

2.5 Example Test Results 108 5.3 Directional Wave Filtering 111 5.3.1 Background Remarks 111 5.3.2 Theory 112 5.3.

3 Calculations 116 5.4 Conclusions 132 Acknowledgments 133 References 133 6 Detection of Geologic Anomalies with Monte Carlo Clustering Assemblies 135 Simon Katz, Fred Aminzadeh, George Chilingar, Leonid Khilyuk and Matin Lockpour 6.1 Introduction 135 6.2 Analysis of Inhomogeneity of the Training and Test Sets and Instability of Clustering 136 6.3 Formation of Multiple Randomized Test Sets and Construction of the Clustering Assemblies 138 6.4 Irregularity Index of Individual Clusters in the Cluster Set 139 6.5 Anomaly Indexes of Individual Records and Clustering Assemblies 141 6.6 Prior and Posterior True and False Discovery Rates for Anomalous and Regular Records 142 6.

7 Estimates of Prior False Discovery Rates for Anomalous Cluster Sets, Clusters, and Individual Records. Permeability Dataset 142 6.8 Posterior Analysis of Efficiency of Anomaly Identification. High Permeability Anomaly 144 6.9 Identification of Records in the Gas Sand Dataset as Anomalous, using Brine Sand Dataset as Data with Regular Records 146 6.10 Notations 149 6.11 Conclusions 149 References 150 7 Dissimilarity Analysis of Petrophysical Parameters as Gas-Sand Predictors 151 Simon Katz, George Chilingar, Fred Aminzadeh and Leonid Khilyuk 7.1 Introduction 152 7.

2 Petrophysical Parameters for Gas-Sand Identification 152 7.3 Lithologic and Fluid Content Dissimilarities of Values of Petrophysical Parameters 154 7.4 Parameter Ranking and Efficiency of Identification of Gas-Sands 155 7.5 ROC Curve Analysis with Cross Validation 159 7.6 Ranking Parameters According to AUC Values 161 7.7 Classification with Multidimensional Parameters as Gas Predictors 163 7.8 Conclusions 164 Definitions and Notations 166 References 166 8 Use of Type Curve for Analyzing Non-Newtonian Fluid Flow Tests Distorted by Wellbore Storage Effects 169 Fahd Siddiqui and Mohamed Y. Soliman 8.

1 Introduction 170 8.2 Objective 173 8.3 Problem Analysis 173 8.3.1 Model Assumptions 174 8.3.2 Solution Without the Wellbore Storage Distortion 175 8.3.

3 Wellbore Storage and Skin Effects 175 8.3.4 Solution by Mathematical Inspection 175 8.3.5 Solution Verification 176 8.4 Use of Finite Element 176 8.5 Analysis Methodology 177 8.5.

1 Finding the n Value 177 8.5.2 Dimensionless Wellbore Storage 178 8.5.3 Use of Type Curves 178 8.5.4 Match Point 179 8.5.

5 Uncertainty in Analysis 180 8.6 Test Data Examples 180 8.6.1 Match Point 182 8.6.2 Match Point 183 8.6.3 Analysis Recommendations 185 8.

6.4 Match Point 185 8.6.5 Analysis Recommendations 186 8.6.6 Match point 186 8.7 Conclusion 188 Nomenclature 188 References 189 Appendix A: Non-Linear Boundary Condition and Laplace Transform 189 Appendix B: Type Curve Charts for Various Power Law Indices 191 Part 3: Reservoir Permeability Detection 195 9 Permeability Prediction Using Machine Learning, Exponential, Multiplicative, and Hybrid Models 197 Simon Katz, Fred Aminzadeh, George Chilingar and M. Lackpour 9.

1 Introduction 197 9.2 Additive, Multiplicative, Exponential, and Hybrid Permeability Models 198 9.3 Combination of Basis Function Expansion and Exhaustive Search for Optimum Subset of Predictors 200 9.4 Outliers in the Forecasts Produced with Four Permeability Models 201 9.5 Additive, Multiplicative, and Exponential Committee Machines 203 9.6 Permeability Forecast with First Level Committee Machines. Sandstone Dataset 206 9.7 Permeability Prediction with First Level Committee Machines.

Carbonate Reservoirs 210 9.8 Analysis of Accuracy of Outlier Replacement by The First and Second Level Committee Machines. Sandstone Dataset 212 9.9 Conclusion 214 Notations and Definitions 215 References 216 10 Geological and Geophysical Criteria for Identifying Zones of High Gas Permeability of Coals (Using the Example of Kuzbass CBM Deposits) 217 A.G. Pogosyan 10.1 Introduction 217 10.2 Physical Properties and External Load Conditions on a Coal Reservoir 219 10.

3 Basis for Evaluating Physical and Mechanical Coalbed Properties in the Borehole Environment 225 10.4 Conclusions 228 Acknowledgement 228 References 229 11 Rock Permeability Forecasts Using Machine Learning and Monte Carlo Committee Machines 231 Simon Katz, Fred Aminzadeh, Wennan Long, George Chilingar and Matin Lackpour 11.1 Introduction 232 11.2 Monte Carlo Cross Validation and Monte Carlo Committee Machines 233 11.3 Performance of Extended MC Cross Validation and Construction MC Committee Machines 236 11.4 Parameters of Distribution of the Number of Individual Forecasts in Monte Carlo Cross Validation 237 11.5 Linear Regression Permeability Forecast with Empirical Permeability Models 238 11.6 Accuracy of the Forecasts with Machine Learning Methods 242 11.

7 Analysis of Instability of the Forecast 244 11.8 Enhancement of Stability of the MC Committee Machines Forecast Via Increase of the Number of Individual Forecasts 246 11.9 Conclusions 247 Nomenclature 247 Appendix 1- Description of Permeability Models from Different Fields 248 Appendix 2- A Brief Overview of Modular Networks or Committee Machines 249 References 251 Part 4: Reserves Evaluation/Decision Making 253 12 The Gulf of Mexico Petroleum System - Foundation for Science-Based Decision Making 255 Corinne Disenhof, MacKenzie Mark-Moser and Kelly Rose Introduction 256 Basin Development and Geologic Overview 257 Petroleum System 259 Reservoir Geology 259 Hydrocarbons 261 Salt and Structure 262 Conclusions 263 Acknowledgments and Disclaimer 264 References 265 13 Forecast and Uncertainty Analysis of Production Decline Trends with Bootstrap and Monte Carlo Modeling 269 Simon Katz, George Chilingar and Leonid Khilyuk 13.1 Introduction 270 13.2 Simulated Decline Curves 271 13.3 Nonlinear Least Squares for Decline Curve Approximation 273 13.4 New Method of Grid.