Reservoir Characterization

Fred Aminzadeh, PhD, is a world-renowned academic and scientist in the energy industry.  With over 20 years of teaching experience at the University of Southern California and at the University of Houston, he also has extensive industry experience not only in oil and gas, but also in geothermal energy and other areas of energy. He also served as the president of Society of Exploration Geophysicists. He has been author of multiple books and has written numerous papers that have been well-received by academics and industry experts alike.  He served as the editor in chief of the journal, The Journal of Sustainable Energy Engineering, formerly of Scrivener Publishing. He is currently editing the series, “Sustainable Energy Engineering,” for the Wiley-Scrivener imprint.

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