RESERVOIR CHARACTERIZATION The second volume in the series, “Sustainable Energy Engineering,” written by some of the foremost authorities in the world on reservoir engineering, this groundbreaking new volume presents the most comprehensive and updated new processes, equipment, and practical applications in the field. Long thought of as not being “sustainable,” newly discovered sources of petroleum and newly developed methods for petroleum extraction have made it clear that not only can the petroleum industry march toward sustainability, but it can be made “greener” and more environmentally friendly. Sustainable energy engineering is where the technical, economic, and environmental aspects of energy production intersect and affect each other. This collection of papers covers the strategic and economic implications of methods used to characterize petroleum reservoirs. Born out of the journal by the same name, formerly published by Scrivener Publishing, most of the articles in this volume have been updated, and there are some new additions, as well, to keep the engineer abreast of any updates and new methods in the industry. Truly a snapshot of the state of the art, this groundbreaking volume is a must-have for any petroleum engineer working in the field, environmental engineers, petroleum engineering students, and any other engineer or scientist working with reservoirs. This outstanding new volume: Is a collection of papers on reservoir characterization written by world-renowned engineers and scientists and presents them here, in one volume Contains in-depth coverage of not just the fundamentals of reservoir characterization, but the anomalies and challenges, set in application-based, real-world situations Covers reservoir characterization for the engineer to be able to solve daily problems on the job, whether in the field or in the office Deconstructs myths that are prevalent and deeply rooted in the industry and reconstructs logical solutions Is a valuable resource for the veteran engineer, new hire, or petroleum engineering student

Table of Contents:
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 Search for Approximation and Forecast of Decline Curves 273 13.5 Iterative Minimization of Least Squares with Multiple Approximating Models 275 13.6 Grid Search Followed by Iterative Minimization with Levenberg-Marquardt Algorithm 276 13.7 Two Methods for Aggregated Forecast and Analysis of Forecast Uncertainty 277 13.8 Uncertainty Quantile Ranges Obtained Using Monte Carlo and Bootstrap Methods 279 13.9 Monte Carlo Forecast and Analysis of Forecast Uncertainty 280 13.10 Block Bootstrap Forecast and Analysis of Forecast Uncertainty 284 13.11 Comparative Analysis of Results of Monte Carlo and Bootstrap Simulations 285 13.12 Conclusions 287 References 288 14 Oil and Gas Company Production, Reserves, and Valuation 289 Mark J. Kaiser 14.1 Introduction 290 14.2 Reserves 292 14.2.1 Proved Reserves 292 14.2.2 Proved Reserves Categories 292 14.2.3 Reserves Reporting 293 14.2.4 Probable and Possible Reserves 293 14.2.5 Contractual Differences 294 14.3 Production 294 14.4 Factors that Impact Company Value 295 14.4.1 Ownership 295 14.4.1.1 International Oil Companies 295 14.4.1.2 National Oil Companies 296 14.4.1.3 Government Sponsored Entities 296 14.4.1.4 Independents and Juniors 297 14.4.2 Degree of Integration 297 14.4.3 Product mix 298 14.4.4 Commodity Price 298 14.4.5 Production Cost 299 14.4.6 Finding Cost 299 14.4.7 Assets 300 14.4.8 Capital Structure 300 14.4.9 Geologic Diversification 301 14.4.10 Geographic Diversification 301 14.4.11 Unobservable Factors 302 14.5 Summary Statistics 303 14.5.1 Sample 303 14.5.2 Variables 303 14.5.3 Data Source 305 14.5.4 International Oil Companies 305 14.5.5 Independents 308 14.6 Market Capitalization 309 14.6.1 Functional Specification 309 14.6.2 Expectations 309 14.7 International Oil Companies 310 14.8 U.S. Independents 312 14.8.1 Large vs. Small Cap, Oil vs. Gas 312 14.8.2 Consolidated Small-Caps 314 14.8.3 Multinational vs. Domestic 314 14.8.4 Conventional vs. Unconventional 315 14.8.5 Production and Reserves 316 14.8.6 Regression Models 316 14.9 Private Companies 318 14.10 National Oil Companies of OPEC 320 14.11 Government Sponsored Enterprises and Other International Companies 320 14.12 Conclusions 323 References 324 Part 5: Unconventional Reservoirs 337 15 An Analytical Thermal-Model for Optimization of Gas-Drilling in Unconventional Tight-Sand Reservoirs 339 Boyun Guo, Gao Li and Jinze Song 15.1 Introduction 340 15.2 Mathematical Model 341 15.3 Model Comparison 346 15.4 Sensitivity Analysis 348 15.5 Model Applications 349 15.6 Conclusions 351 Nomenclature 352 Acknowledgements 353 References 353 Appendix A: Steady Heat Transfer Solution for Fluid Temperature in Counter-Current Flow 355 Assumptions 355 Governing Equation 355 Boundary Conditions 360 Solution 360 16 Development of an Analytical Model for Predicting the Fluid Temperature Profile in Drilling Gas Hydrates Reservoirs 363 Liqun Shan, Boyun Guo and Xiao Cai 16.1 Introduction 364 16.2 Mathematical Model 365 16.3 Case Study 373 16.4 Sensitivity Analysis 374 16.5 Conclusions 377 Acknowledgements 378 Nomenclature 378 References 379 17 Distinguishing Between Brine-Saturated and Gas-Saturated Shaly Formations with a Monte-Carlo Simulation of Seismic Velocities 383 Simon Katz, George Chilingar and Leonid Khilyuk 17.1 Introduction 384 17.2 Random Models for Seismic Velocities 385 17.3 Variability of Seismic Velocities Predicted by Random Models 387 17.4 The Separability of (Vp , Vs ) Clusters for Gas- and Brine-Saturated Formations 388 17.5 Reliability Analysis of Identifying Gas-Filled Formations 389 17.5.1 Classification with K-Nearest Neighbor 391 17.5.2 Classification with Recursive Partitioning 392 17.5.3 Classification with Linear Discriminant Analysis 394 17.5.4 Comparison of the Three Classification Techniques 395 17.6 Conclusions 396 References 397 18 Shale Mechanical Properties Influence Factors Overview and Experimental Investigation on Water Content Effects 399 Hui Li, Bitao Lai and Shuhua Lin 18.1 Introduction 400 18.2 Influence Factors 400 18.2.1 Effective Pressure 401 18.2.2 Porosity 402 18.2.3 Water Content 403 18.2.4 Salt Solutions 405 18.2.5 Total Organic Carbon (TOC) 406 18.2.6 Clay Content 407 18.2.7 Bedding Plane Orientation 408 18.2.8 Mineralogy 411 18.2.9 Anisotropy 413 18.2.10 Temperature 413 18.3 Experimental Investigation of Water Saturation Effects on Shale’s Mechanical Properties 414 18.3.1 Experiment Description 414 18.3.2 Results and Discussion 414 18.3.3 Error Analysis of Experiments 417 18.4 Conclusions 418 Acknowledgements 420 References 420 Part 6: Enhance Oil Recovery 427 19 A Numerical Investigation of Enhanced Oil Recovery Using Hydrophilic Nanofuids 429 Yin Feng, Liyuan Cao and Erxiu Shi 19.1 Introduction 430 19.2 Simulation Framework 432 19.2.1 Background 432 19.2.2 Two Essential Computational Components 433 19.2.2.1 Flow Model 433 19.2.2.2 Nanoparticle Transport and Retention Model 435 19.3 Coupling of Mathematical Models 437 19.4 Verification Cases 439 19.4.1 Effect of Time Steps on the Performance of the in House Simulator 439 19.4.2 Comparison with Eclipse 440 19.4.3 Comparison with Software MNM1D 442 19.5 Results 443 19.5.1 Continuous Injection 445 19.5.1.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption 445 19.5.1.2 Effect of Injection Rate on Oil Recovery and Nanoparticle Adsorption 447 19.5.2 Slug Injection 449 19.5.2.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption 449 19.5.2.2 Effect of Slug Size on Oil Recovery and Nanoparticle Adsorption 451 19.5.3 Water Postflush 452 19.5.3.1 Effect of Injection Time Length 452 19.5.3.2 Effect of Flow Rate Ratio Between Water and Nanofuids on Oil and Nanoparticle Recovery 452 19.5.4 3D Model Showcase 455 19.6 Discussions 457 19.7 Conclusions and Future Work 459 References 461 20 3D Seismic-Assisted CO2 -EOR Flow Simulation for the Tensleep Formation at Teapot Dome, USA 463 Payam Kavousi Ghahfarokhi, Thomas H. Wilson and Alan Lee Brown 20.1 Presentation Sequence 464 20.2 Introduction 464 20.3 Geological Background 468 20.4 Discrete Fracture Network (DFN) 469 20.5 Petrophysical Modeling 473 20.6 PVT Analysis 473 20.7 Streamline Analysis 479 20.8 Co2 -EOR 479 20.9 Conclusions 483 Acknowledgement 483 References 484 Part 7: New Advances in Reservoir Characterization-Machine Learning Applications 487 21 Application of Machine Learning in Reservoir Characterization 489 Fred Aminzadeh 21.1 Brief Introduction to Reservoir Characterization 489 21.2 Artificial Intelligence and Machine (Deep) Learning Review 491 21.2.1 Support Vector Machines 492 21.2.2 Clustering (Unsupervised Classification) 492 21.2.3 Ensemble Methods 497 21.2.4 Artificial Neural Networks (ANN)- Based Methods 498 21.3 Artificial Intelligence and Machine (Deep) Learning Applications to Reservoir Characterization 502 21.3.1 3D Structural Model Development 503 21.3.2 Sedimentary Modeling 506 21.3.3 3D Petrophysical Modeling 508 21.3.4 Dynamic Modeling and Simulations 512 21.4 Machine (Deep) Learning and Enhanced Oil Recovery (EOR) 513 21.4.1 ANNs for EOR Performance and Economics 514 21.4.2 ANNs for EOR Screening 516 21.5 Conclusion 517 Acknowledgement 518 References 518 Index 525