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 xixPreface xxiiiPart 1: Introduction 11 Reservoir Characterization: Fundamental and Applications - An Overview 3Fred Aminzadeh1.1 Introduction to Reservoir Characterization? 31.2 Data Requirements for Reservoir Characterization 51.3 SURE Challenge 71.4 Reservoir Characterization in the Exploration, Development and Production Phases 101.4.1 Exploration Stage/Development Stage 101.4.2 Primary Production Stage 111.4.3 Secondary/Tertiary Production Stage 111.5 Dynamic Reservoir Characterization (DRC) 121.5.1 4D Seismic for DRC 131.5.2 Microseismic Data for DRC 141.6 More on Reservoir Characterization and Reservoir Modeling for Reservoir Simulation 151.6.1 Rock Physics 161.6.2 Reservoir Modeling 171.7 Conclusion 20References 20Part 2: General Reservoir Characterization and Anomaly Detection 232 A Comparison Between Estimated Shear Wave Velocity and Elastic Modulus by Empirical Equations and that of Laboratory Measurements at Reservoir Pressure Condition 25Haleh Azizia, Hamid Reza Siahkoohi, Brian Evans, Nasser Keshavarz Farajkhah and Ezatollah KazemZadeh2.1 Introduction 262.2 Methodology 282.1.2 Estimating the Shear Wave Velocity 282.2.2 Estimating Geomechanical Parameters 312.3 Laboratory Set Up and Measurements 322.3.1 Laboratory Data Collection 342.4 Results and Discussion 352.5 Conclusions 412.6 Acknowledgment 43References 433 Anomaly Detection within Homogenous Geologic Area 47Simon Katz, Fred Aminzadeh, George Chilingar and Leonid Khilyuk3.1 Introduction 483.2 Anomaly Detection Methodology 493.3 Basic Anomaly Detection Classifiers 503.4 Prior and Posterior Characteristics of Anomaly Detection Performance 523.5 ROC Curve Analysis 553.6 Optimization of Aggregated AD Classifier Using Part of the Anomaly Identified by Universal Classifiers 583.7 Bootstrap Based Tests of Anomaly Type Hypothesis 613.8 Conclusion 64References 654 Characterization of Carbonate Source-Derived Hydrocarbons Using Advanced Geochemical Technologies 69Hossein Alimi4.1 Introduction 704.2 Samples and Analyses Performed 714.3 Results and Discussions 724.4 Summary and Conclusions 79References 805 Strategies in High-Data-Rate MWD Mud Pulse Telemetry 81Yinao Su, Limin Sheng, Lin Li, Hailong Bian, Rong Shi, Xiaoying Zhuang and Wilson Chin5.1 Summary 825.1.1 High Data Rates and Energy Sustainability 825.1.2 Introduction 835.1.3 MWD Telemetry Basics 855.1.4 New Telemetry Approach 875.2 New Technology Elements 885.2.1 Downhole Source and Signal Optimization 895.2.2 Surface Signal Processing and Noise Removal 925.2.3 Pressure, Torque and Erosion Computer Modeling 935.2.4 Wind Tunnel Analysis: Studying New Approaches 965.2.5 Example Test Results 1085.3 Directional Wave Filtering 1115.3.1 Background Remarks 1115.3.2 Theory 1125.3.3 Calculations 1165.4 Conclusions 132Acknowledgments 133References 1336 Detection of Geologic Anomalies with Monte Carlo Clustering Assemblies 135Simon Katz, Fred Aminzadeh, George Chilingar, Leonid Khilyuk and Matin Lockpour6.1 Introduction 1356.2 Analysis of Inhomogeneity of the Training and Test Sets and Instability of Clustering 1366.3 Formation of Multiple Randomized Test Sets and Construction of the Clustering Assemblies 1386.4 Irregularity Index of Individual Clusters in the Cluster Set 1396.5 Anomaly Indexes of Individual Records and Clustering Assemblies 1416.6 Prior and Posterior True and False Discovery Rates for Anomalous and Regular Records 1426.7 Estimates of Prior False Discovery Rates for Anomalous Cluster Sets, Clusters, and Individual Records. Permeability Dataset 1426.8 Posterior Analysis of Efficiency of Anomaly Identification. High Permeability Anomaly 1446.9 Identification of Records in the Gas Sand Dataset as Anomalous, using Brine Sand Dataset as Data with Regular Records 1466.10 Notations 1496.11 Conclusions 149References 1507 Dissimilarity Analysis of Petrophysical Parameters as Gas-Sand Predictors 151Simon Katz, George Chilingar, Fred Aminzadeh and Leonid Khilyuk7.1 Introduction 1527.2 Petrophysical Parameters for Gas-Sand Identification 1527.3 Lithologic and Fluid Content Dissimilarities of Values of Petrophysical Parameters 1547.4 Parameter Ranking and Efficiency of Identification of Gas-Sands 1557.5 ROC Curve Analysis with Cross Validation 1597.6 Ranking Parameters According to AUC Values 1617.7 Classification with Multidimensional Parameters as Gas Predictors 1637.8 Conclusions 164Definitions and Notations 166References 1668 Use of Type Curve for Analyzing Non-Newtonian Fluid Flow Tests Distorted by Wellbore Storage Effects 169Fahd Siddiqui and Mohamed Y. Soliman8.1 Introduction 1708.2 Objective 1738.3 Problem Analysis 1738.3.1 Model Assumptions 1748.3.2 Solution Without the Wellbore Storage Distortion 1758.3.3 Wellbore Storage and Skin Effects 1758.3.4 Solution by Mathematical Inspection 1758.3.5 Solution Verification 1768.4 Use of Finite Element 1768.5 Analysis Methodology 1778.5.1 Finding the n Value 1778.5.2 Dimensionless Wellbore Storage 1788.5.3 Use of Type Curves 1788.5.4 Match Point 1798.5.5 Uncertainty in Analysis 1808.6 Test Data Examples 1808.6.1 Match Point 1828.6.2 Match Point 1838.6.3 Analysis Recommendations 1858.6.4 Match Point 1858.6.5 Analysis Recommendations 1868.6.6 Match point 1868.7 Conclusion 188Nomenclature 188References 189Appendix A: Non-Linear Boundary Condition and Laplace Transform 189Appendix B: Type Curve Charts for Various Power Law Indices 191Part 3: Reservoir Permeability Detection 1959 Permeability Prediction Using Machine Learning, Exponential, Multiplicative, and Hybrid Models 197Simon Katz, Fred Aminzadeh, George Chilingar and M. Lackpour9.1 Introduction 1979.2 Additive, Multiplicative, Exponential, and Hybrid Permeability Models 1989.3 Combination of Basis Function Expansion and Exhaustive Search for Optimum Subset of Predictors 2009.4 Outliers in the Forecasts Produced with Four Permeability Models 2019.5 Additive, Multiplicative, and Exponential Committee Machines 2039.6 Permeability Forecast with First Level Committee Machines. Sandstone Dataset 2069.7 Permeability Prediction with First Level Committee Machines. Carbonate Reservoirs 2109.8 Analysis of Accuracy of Outlier Replacement by The First and Second Level Committee Machines. Sandstone Dataset 2129.9 Conclusion 214Notations and Definitions 215References 21610 Geological and Geophysical Criteria for Identifying Zones of High Gas Permeability of Coals (Using the Example of Kuzbass CBM Deposits) 217A.G. Pogosyan10.1 Introduction 21710.2 Physical Properties and External Load Conditions on a Coal Reservoir 21910.3 Basis for Evaluating Physical and Mechanical Coalbed Properties in the Borehole Environment 22510.4 Conclusions 228Acknowledgement 228References 22911 Rock Permeability Forecasts Using Machine Learning and Monte Carlo Committee Machines 231Simon Katz, Fred Aminzadeh, Wennan Long, George Chilingar and Matin Lackpour11.1 Introduction 23211.2 Monte Carlo Cross Validation and Monte Carlo Committee Machines 23311.3 Performance of Extended MC Cross Validation and Construction MC Committee Machines 23611.4 Parameters of Distribution of the Number of Individual Forecasts in Monte Carlo Cross Validation 23711.5 Linear Regression Permeability Forecast with Empirical Permeability Models 23811.6 Accuracy of the Forecasts with Machine Learning Methods 24211.7 Analysis of Instability of the Forecast 24411.8 Enhancement of Stability of the MC Committee Machines Forecast Via Increase of the Number of Individual Forecasts 24611.9 Conclusions 247Nomenclature 247Appendix 1- Description of Permeability Models from Different Fields 248Appendix 2- A Brief Overview of Modular Networks or Committee Machines 249References 251Part 4: Reserves Evaluation/Decision Making 25312 The Gulf of Mexico Petroleum System – Foundation for Science-Based Decision Making 255Corinne Disenhof, MacKenzie Mark-Moser and Kelly RoseIntroduction 256Basin Development and Geologic Overview 257Petroleum System 259Reservoir Geology 259Hydrocarbons 261Salt and Structure 262Conclusions 263Acknowledgments and Disclaimer 264References 26513 Forecast and Uncertainty Analysis of Production Decline Trends with Bootstrap and Monte Carlo Modeling 269Simon Katz, George Chilingar and Leonid Khilyuk13.1 Introduction 27013.2 Simulated Decline Curves 27113.3 Nonlinear Least Squares for Decline Curve Approximation 27313.4 New Method of Grid Search for Approximation and Forecast of Decline Curves 27313.5 Iterative Minimization of Least Squares with Multiple Approximating Models 27513.6 Grid Search Followed by Iterative Minimization with Levenberg-Marquardt Algorithm 27613.7 Two Methods for Aggregated Forecast and Analysis of Forecast Uncertainty 27713.8 Uncertainty Quantile Ranges Obtained Using Monte Carlo and Bootstrap Methods 27913.9 Monte Carlo Forecast and Analysis of Forecast Uncertainty 28013.10 Block Bootstrap Forecast and Analysis of Forecast Uncertainty 28413.11 Comparative Analysis of Results of Monte Carlo and Bootstrap Simulations 28513.12 Conclusions 287References 28814 Oil and Gas Company Production, Reserves, and Valuation 289Mark J. Kaiser14.1 Introduction 29014.2 Reserves 29214.2.1 Proved Reserves 29214.2.2 Proved Reserves Categories 29214.2.3 Reserves Reporting 29314.2.4 Probable and Possible Reserves 29314.2.5 Contractual Differences 29414.3 Production 29414.4 Factors that Impact Company Value 29514.4.1 Ownership 29514.4.1.1 International Oil Companies 29514.4.1.2 National Oil Companies 29614.4.1.3 Government Sponsored Entities 29614.4.1.4 Independents and Juniors 29714.4.2 Degree of Integration 29714.4.3 Product mix 29814.4.4 Commodity Price 29814.4.5 Production Cost 29914.4.6 Finding Cost 29914.4.7 Assets 30014.4.8 Capital Structure 30014.4.9 Geologic Diversification 30114.4.10 Geographic Diversification 30114.4.11 Unobservable Factors 30214.5 Summary Statistics 30314.5.1 Sample 30314.5.2 Variables 30314.5.3 Data Source 30514.5.4 International Oil Companies 30514.5.5 Independents 30814.6 Market Capitalization 30914.6.1 Functional Specification 30914.6.2 Expectations 30914.7 International Oil Companies 31014.8 U.S. Independents 31214.8.1 Large vs. Small Cap, Oil vs. Gas 31214.8.2 Consolidated Small-Caps 31414.8.3 Multinational vs. Domestic 31414.8.4 Conventional vs. Unconventional 31514.8.5 Production and Reserves 31614.8.6 Regression Models 31614.9 Private Companies 31814.10 National Oil Companies of OPEC 32014.11 Government Sponsored Enterprises and Other International Companies 32014.12 Conclusions 323References 324Part 5: Unconventional Reservoirs 33715 An Analytical Thermal-Model for Optimization of Gas-Drilling in Unconventional Tight-Sand Reservoirs 339Boyun Guo, Gao Li and Jinze Song15.1 Introduction 34015.2 Mathematical Model 34115.3 Model Comparison 34615.4 Sensitivity Analysis 34815.5 Model Applications 34915.6 Conclusions 351Nomenclature 352Acknowledgements 353References 353Appendix A: Steady Heat Transfer Solution for Fluid Temperature in Counter-Current Flow 355Assumptions 355Governing Equation 355Boundary Conditions 360Solution 36016 Development of an Analytical Model for Predicting the Fluid Temperature Profile in Drilling Gas Hydrates Reservoirs 363Liqun Shan, Boyun Guo and Xiao Cai16.1 Introduction 36416.2 Mathematical Model 36516.3 Case Study 37316.4 Sensitivity Analysis 37416.5 Conclusions 377Acknowledgements 378Nomenclature 378References 37917 Distinguishing Between Brine-Saturated and Gas-Saturated Shaly Formations with a Monte-Carlo Simulation of Seismic Velocities 383Simon Katz, George Chilingar and Leonid Khilyuk17.1 Introduction 38417.2 Random Models for Seismic Velocities 38517.3 Variability of Seismic Velocities Predicted by Random Models 38717.4 The Separability of (Vp , Vs ) Clusters for Gas- and Brine-Saturated Formations 38817.5 Reliability Analysis of Identifying Gas-Filled Formations 38917.5.1 Classification with K-Nearest Neighbor 39117.5.2 Classification with Recursive Partitioning 39217.5.3 Classification with Linear Discriminant Analysis 39417.5.4 Comparison of the Three Classification Techniques 39517.6 Conclusions 396References 39718 Shale Mechanical Properties Influence Factors Overview and Experimental Investigation on Water Content Effects 399Hui Li, Bitao Lai and Shuhua Lin18.1 Introduction 40018.2 Influence Factors 40018.2.1 Effective Pressure 40118.2.2 Porosity 40218.2.3 Water Content 40318.2.4 Salt Solutions 40518.2.5 Total Organic Carbon (TOC) 40618.2.6 Clay Content 40718.2.7 Bedding Plane Orientation 40818.2.8 Mineralogy 41118.2.9 Anisotropy 41318.2.10 Temperature 41318.3 Experimental Investigation of Water Saturation Effects on Shale’s Mechanical Properties 41418.3.1 Experiment Description 41418.3.2 Results and Discussion 41418.3.3 Error Analysis of Experiments 41718.4 Conclusions 418Acknowledgements 420References 420Part 6: Enhance Oil Recovery 42719 A Numerical Investigation of Enhanced Oil Recovery Using Hydrophilic Nanofuids 429Yin Feng, Liyuan Cao and Erxiu Shi19.1 Introduction 43019.2 Simulation Framework 43219.2.1 Background 43219.2.2 Two Essential Computational Components 43319.2.2.1 Flow Model 43319.2.2.2 Nanoparticle Transport and Retention Model 43519.3 Coupling of Mathematical Models 43719.4 Verification Cases 43919.4.1 Effect of Time Steps on the Performance of the in House Simulator 43919.4.2 Comparison with Eclipse 44019.4.3 Comparison with Software MNM1D 44219.5 Results 44319.5.1 Continuous Injection 44519.5.1.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption 44519.5.1.2 Effect of Injection Rate on Oil Recovery and Nanoparticle Adsorption 44719.5.2 Slug Injection 44919.5.2.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption 44919.5.2.2 Effect of Slug Size on Oil Recovery and Nanoparticle Adsorption 45119.5.3 Water Postflush 45219.5.3.1 Effect of Injection Time Length 45219.5.3.2 Effect of Flow Rate Ratio Between Water and Nanofuids on Oil and Nanoparticle Recovery 45219.5.4 3D Model Showcase 45519.6 Discussions 45719.7 Conclusions and Future Work 459References 46120 3D Seismic-Assisted CO2 -EOR Flow Simulation for the Tensleep Formation at Teapot Dome, USA 463Payam Kavousi Ghahfarokhi, Thomas H. Wilson and Alan Lee Brown20.1 Presentation Sequence 46420.2 Introduction 46420.3 Geological Background 46820.4 Discrete Fracture Network (DFN) 46920.5 Petrophysical Modeling 47320.6 PVT Analysis 47320.7 Streamline Analysis 47920.8 Co2 -EOR 47920.9 Conclusions 483Acknowledgement 483References 484Part 7: New Advances in Reservoir Characterization-Machine Learning Applications 48721 Application of Machine Learning in Reservoir Characterization 489Fred Aminzadeh21.1 Brief Introduction to Reservoir Characterization 48921.2 Artificial Intelligence and Machine (Deep) Learning Review 49121.2.1 Support Vector Machines 49221.2.2 Clustering (Unsupervised Classification) 49221.2.3 Ensemble Methods 49721.2.4 Artificial Neural Networks (ANN)- Based Methods 49821.3 Artificial Intelligence and Machine (Deep) Learning Applications to Reservoir Characterization 50221.3.1 3D Structural Model Development 50321.3.2 Sedimentary Modeling 50621.3.3 3D Petrophysical Modeling 50821.3.4 Dynamic Modeling and Simulations 51221.4 Machine (Deep) Learning and Enhanced Oil Recovery (EOR) 51321.4.1 ANNs for EOR Performance and Economics 51421.4.2 ANNs for EOR Screening 51621.5 Conclusion 517Acknowledgement 518References 518Index 525