{"product_id":"reservoir-characterization-9781119556213","title":"Reservoir Characterization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eRESERVOIR CHARACTERIZATION\u003c\/b\u003e \u003cp\u003e\u003cb\u003eThe 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. \u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eLong 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.  \u003c\/p\u003e\u003cp\u003eThis 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 arti\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eForeword xix\u003c\/p\u003e \u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Reservoir Characterization: Fundamental and Applications - An Overview 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFred Aminzadeh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction to Reservoir Characterization? 3\u003c\/p\u003e \u003cp\u003e1.2 Data Requirements for Reservoir Characterization 5\u003c\/p\u003e \u003cp\u003e1.3 SURE Challenge 7\u003c\/p\u003e \u003cp\u003e1.4 Reservoir Characterization in the Exploration, Development and Production Phases 10\u003c\/p\u003e \u003cp\u003e1.4.1 Exploration Stage\/Development Stage 10\u003c\/p\u003e \u003cp\u003e1.4.2 Primary Production Stage 11\u003c\/p\u003e \u003cp\u003e1.4.3 Secondary\/Tertiary Production Stage 11\u003c\/p\u003e \u003cp\u003e1.5 Dynamic Reservoir Characterization (DRC) 12\u003c\/p\u003e \u003cp\u003e1.5.1 4D Seismic for DRC 13\u003c\/p\u003e \u003cp\u003e1.5.2 Microseismic Data for DRC 14\u003c\/p\u003e \u003cp\u003e1.6 More on Reservoir Characterization and Reservoir Modeling for Reservoir Simulation 15\u003c\/p\u003e \u003cp\u003e1.6.1 Rock Physics 16\u003c\/p\u003e \u003cp\u003e1.6.2 Reservoir Modeling 17\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 20\u003c\/p\u003e \u003cp\u003eReferences 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: General Reservoir Characterization and Anomaly Detection 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A Comparison Between Estimated Shear Wave Velocity and Elastic Modulus by Empirical Equations and that of Laboratory Measurements at Reservoir Pressure Condition 25\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHaleh Azizia, Hamid Reza Siahkoohi, Brian Evans, Nasser Keshavarz Farajkhah and Ezatollah KazemZadeh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 26\u003c\/p\u003e \u003cp\u003e2.2 Methodology 28\u003c\/p\u003e \u003cp\u003e2.1.2 Estimating the Shear Wave Velocity 28\u003c\/p\u003e \u003cp\u003e2.2.2 Estimating Geomechanical Parameters 31\u003c\/p\u003e \u003cp\u003e2.3 Laboratory Set Up and Measurements 32\u003c\/p\u003e \u003cp\u003e2.3.1 Laboratory Data Collection 34\u003c\/p\u003e \u003cp\u003e2.4 Results and Discussion 35\u003c\/p\u003e \u003cp\u003e2.5 Conclusions 41\u003c\/p\u003e \u003cp\u003e2.6 Acknowledgment 43\u003c\/p\u003e \u003cp\u003eReferences 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Anomaly Detection within Homogenous Geologic Area 47\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSimon Katz, Fred Aminzadeh, George Chilingar and Leonid Khilyuk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 48\u003c\/p\u003e \u003cp\u003e3.2 Anomaly Detection Methodology 49\u003c\/p\u003e \u003cp\u003e3.3 Basic Anomaly Detection Classifiers 50\u003c\/p\u003e \u003cp\u003e3.4 Prior and Posterior Characteristics of Anomaly Detection Performance 52\u003c\/p\u003e \u003cp\u003e3.5 ROC Curve Analysis 55\u003c\/p\u003e \u003cp\u003e3.6 Optimization of Aggregated AD Classifier Using Part of the Anomaly Identified by Universal Classifiers 58\u003c\/p\u003e \u003cp\u003e3.7 Bootstrap Based Tests of Anomaly Type Hypothesis 61\u003c\/p\u003e \u003cp\u003e3.8 Conclusion 64\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Characterization of Carbonate Source-Derived Hydrocarbons Using Advanced Geochemical Technologies 69\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHossein Alimi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 70\u003c\/p\u003e \u003cp\u003e4.2 Samples and Analyses Performed 71\u003c\/p\u003e \u003cp\u003e4.3 Results and Discussions 72\u003c\/p\u003e \u003cp\u003e4.4 Summary and Conclusions 79\u003c\/p\u003e \u003cp\u003eReferences 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Strategies in High-Data-Rate MWD Mud Pulse Telemetry 81\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYinao Su, Limin Sheng, Lin Li, Hailong Bian, Rong Shi, Xiaoying Zhuang and Wilson Chin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Summary 82\u003c\/p\u003e \u003cp\u003e5.1.1 High Data Rates and Energy Sustainability 82\u003c\/p\u003e \u003cp\u003e5.1.2 Introduction 83\u003c\/p\u003e \u003cp\u003e5.1.3 MWD Telemetry Basics 85\u003c\/p\u003e \u003cp\u003e5.1.4 New Telemetry Approach 87\u003c\/p\u003e \u003cp\u003e5.2 New Technology Elements 88\u003c\/p\u003e \u003cp\u003e5.2.1 Downhole Source and Signal Optimization 89\u003c\/p\u003e \u003cp\u003e5.2.2 Surface Signal Processing and Noise Removal 92\u003c\/p\u003e \u003cp\u003e5.2.3 Pressure, Torque and Erosion Computer Modeling 93\u003c\/p\u003e \u003cp\u003e5.2.4 Wind Tunnel Analysis: Studying New Approaches 96\u003c\/p\u003e \u003cp\u003e5.2.5 Example Test Results 108\u003c\/p\u003e \u003cp\u003e5.3 Directional Wave Filtering 111\u003c\/p\u003e \u003cp\u003e5.3.1 Background Remarks 111\u003c\/p\u003e \u003cp\u003e5.3.2 Theory 112\u003c\/p\u003e \u003cp\u003e5.3.3 Calculations 116\u003c\/p\u003e \u003cp\u003e5.4 Conclusions 132\u003c\/p\u003e \u003cp\u003eAcknowledgments 133\u003c\/p\u003e \u003cp\u003eReferences 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Detection of Geologic Anomalies with Monte Carlo Clustering Assemblies 135\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSimon Katz, Fred Aminzadeh, George Chilingar, Leonid Khilyuk and Matin Lockpour\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 135\u003c\/p\u003e \u003cp\u003e6.2 Analysis of Inhomogeneity of the Training and Test Sets and Instability of Clustering 136\u003c\/p\u003e \u003cp\u003e6.3 Formation of Multiple Randomized Test Sets and Construction of the Clustering Assemblies 138\u003c\/p\u003e \u003cp\u003e6.4 Irregularity Index of Individual Clusters in the Cluster Set 139\u003c\/p\u003e \u003cp\u003e6.5 Anomaly Indexes of Individual Records and Clustering Assemblies 141\u003c\/p\u003e \u003cp\u003e6.6 Prior and Posterior True and False Discovery Rates for Anomalous and Regular Records 142\u003c\/p\u003e \u003cp\u003e6.7 Estimates of Prior False Discovery Rates for Anomalous Cluster Sets, Clusters, and Individual Records. Permeability Dataset 142\u003c\/p\u003e \u003cp\u003e6.8 Posterior Analysis of Efficiency of Anomaly Identification. High Permeability Anomaly 144\u003c\/p\u003e \u003cp\u003e6.9 Identification of Records in the Gas Sand Dataset as Anomalous, using Brine Sand Dataset as Data with Regular Records 146\u003c\/p\u003e \u003cp\u003e6.10 Notations 149\u003c\/p\u003e \u003cp\u003e6.11 Conclusions 149\u003c\/p\u003e \u003cp\u003eReferences 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Dissimilarity Analysis of Petrophysical Parameters as Gas-Sand Predictors 151\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSimon Katz, George Chilingar, Fred Aminzadeh and Leonid Khilyuk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 152\u003c\/p\u003e \u003cp\u003e7.2 Petrophysical Parameters for Gas-Sand Identification 152\u003c\/p\u003e \u003cp\u003e7.3 Lithologic and Fluid Content Dissimilarities of Values of Petrophysical Parameters 154\u003c\/p\u003e \u003cp\u003e7.4 Parameter Ranking and Efficiency of Identification of Gas-Sands 155\u003c\/p\u003e \u003cp\u003e7.5 ROC Curve Analysis with Cross Validation 159\u003c\/p\u003e \u003cp\u003e7.6 Ranking Parameters According to AUC Values 161\u003c\/p\u003e \u003cp\u003e7.7 Classification with Multidimensional Parameters as Gas Predictors 163\u003c\/p\u003e \u003cp\u003e7.8 Conclusions 164\u003c\/p\u003e \u003cp\u003eDefinitions and Notations 166\u003c\/p\u003e \u003cp\u003eReferences 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Use of Type Curve for Analyzing Non-Newtonian Fluid Flow Tests Distorted by Wellbore Storage Effects 169\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFahd Siddiqui and Mohamed Y. Soliman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 170\u003c\/p\u003e \u003cp\u003e8.2 Objective 173\u003c\/p\u003e \u003cp\u003e8.3 Problem Analysis 173\u003c\/p\u003e \u003cp\u003e8.3.1 Model Assumptions 174\u003c\/p\u003e \u003cp\u003e8.3.2 Solution Without the Wellbore Storage Distortion 175\u003c\/p\u003e \u003cp\u003e8.3.3 Wellbore Storage and Skin Effects 175\u003c\/p\u003e \u003cp\u003e8.3.4 Solution by Mathematical Inspection 175\u003c\/p\u003e \u003cp\u003e8.3.5 Solution Verification 176\u003c\/p\u003e \u003cp\u003e8.4 Use of Finite Element 176\u003c\/p\u003e \u003cp\u003e8.5 Analysis Methodology 177\u003c\/p\u003e \u003cp\u003e8.5.1 Finding the n Value 177\u003c\/p\u003e \u003cp\u003e8.5.2 Dimensionless Wellbore Storage 178\u003c\/p\u003e \u003cp\u003e8.5.3 Use of Type Curves 178\u003c\/p\u003e \u003cp\u003e8.5.4 Match Point 179\u003c\/p\u003e \u003cp\u003e8.5.5 Uncertainty in Analysis 180\u003c\/p\u003e \u003cp\u003e8.6 Test Data Examples 180\u003c\/p\u003e \u003cp\u003e8.6.1 Match Point 182\u003c\/p\u003e \u003cp\u003e8.6.2 Match Point 183\u003c\/p\u003e \u003cp\u003e8.6.3 Analysis Recommendations 185\u003c\/p\u003e \u003cp\u003e8.6.4 Match Point 185\u003c\/p\u003e \u003cp\u003e8.6.5 Analysis Recommendations 186\u003c\/p\u003e \u003cp\u003e8.6.6 Match point 186\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 188\u003c\/p\u003e \u003cp\u003eNomenclature 188\u003c\/p\u003e \u003cp\u003eReferences 189\u003c\/p\u003e \u003cp\u003eAppendix A: Non-Linear Boundary Condition and Laplace Transform 189\u003c\/p\u003e \u003cp\u003eAppendix B: Type Curve Charts for Various Power Law Indices 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Reservoir Permeability Detection 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Permeability Prediction Using Machine Learning, Exponential, Multiplicative, and Hybrid Models 197\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSimon Katz, Fred Aminzadeh, George Chilingar and M. Lackpour\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 197\u003c\/p\u003e \u003cp\u003e9.2 Additive, Multiplicative, Exponential, and Hybrid Permeability Models 198\u003c\/p\u003e \u003cp\u003e9.3 Combination of Basis Function Expansion and Exhaustive Search for Optimum Subset of Predictors 200\u003c\/p\u003e \u003cp\u003e9.4 Outliers in the Forecasts Produced with Four Permeability Models 201\u003c\/p\u003e \u003cp\u003e9.5 Additive, Multiplicative, and Exponential Committee Machines 203\u003c\/p\u003e \u003cp\u003e9.6 Permeability Forecast with First Level Committee Machines. Sandstone Dataset 206\u003c\/p\u003e \u003cp\u003e9.7 Permeability Prediction with First Level Committee Machines. Carbonate Reservoirs 210\u003c\/p\u003e \u003cp\u003e9.8 Analysis of Accuracy of Outlier Replacement by The First and Second Level Committee Machines. Sandstone Dataset 212\u003c\/p\u003e \u003cp\u003e9.9 Conclusion 214\u003c\/p\u003e \u003cp\u003eNotations and Definitions 215\u003c\/p\u003e \u003cp\u003eReferences 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Geological and Geophysical Criteria for Identifying Zones of High Gas Permeability of Coals (Using the Example of Kuzbass CBM Deposits) 217\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eA.G. Pogosyan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 217\u003c\/p\u003e \u003cp\u003e10.2 Physical Properties and External Load Conditions on a Coal Reservoir 219\u003c\/p\u003e \u003cp\u003e10.3 Basis for Evaluating Physical and Mechanical Coalbed Properties in the Borehole Environment 225\u003c\/p\u003e \u003cp\u003e10.4 Conclusions 228\u003c\/p\u003e \u003cp\u003eAcknowledgement 228\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Rock Permeability Forecasts Using Machine Learning and Monte Carlo Committee Machines 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSimon Katz, Fred Aminzadeh, Wennan Long, George Chilingar and Matin Lackpour\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 232\u003c\/p\u003e \u003cp\u003e11.2 Monte Carlo Cross Validation and Monte Carlo Committee Machines 233\u003c\/p\u003e \u003cp\u003e11.3 Performance of Extended MC Cross Validation and Construction MC Committee Machines 236\u003c\/p\u003e \u003cp\u003e11.4 Parameters of Distribution of the Number of Individual Forecasts in Monte Carlo Cross Validation 237\u003c\/p\u003e \u003cp\u003e11.5 Linear Regression Permeability Forecast with Empirical Permeability Models 238\u003c\/p\u003e \u003cp\u003e11.6 Accuracy of the Forecasts with Machine Learning Methods 242\u003c\/p\u003e \u003cp\u003e11.7 Analysis of Instability of the Forecast 244\u003c\/p\u003e \u003cp\u003e11.8 Enhancement of Stability of the MC Committee Machines Forecast Via Increase of the Number of Individual Forecasts 246\u003c\/p\u003e \u003cp\u003e11.9 Conclusions 247\u003c\/p\u003e \u003cp\u003eNomenclature 247\u003c\/p\u003e \u003cp\u003eAppendix 1- Description of Permeability Models from Different Fields 248\u003c\/p\u003e \u003cp\u003eAppendix 2- A Brief Overview of Modular Networks or Committee Machines 249\u003c\/p\u003e \u003cp\u003eReferences 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Reserves Evaluation\/Decision Making 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 The Gulf of Mexico Petroleum System – Foundation for Science-Based Decision Making 255\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eCorinne Disenhof, MacKenzie Mark-Moser and Kelly Rose\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 256\u003c\/p\u003e \u003cp\u003eBasin Development and Geologic Overview 257\u003c\/p\u003e \u003cp\u003ePetroleum System 259\u003c\/p\u003e \u003cp\u003eReservoir Geology 259\u003c\/p\u003e \u003cp\u003eHydrocarbons 261\u003c\/p\u003e \u003cp\u003eSalt and Structure 262\u003c\/p\u003e \u003cp\u003eConclusions 263\u003c\/p\u003e \u003cp\u003eAcknowledgments and Disclaimer 264\u003c\/p\u003e \u003cp\u003eReferences 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Forecast and Uncertainty Analysis of Production Decline Trends with Bootstrap and Monte Carlo Modeling 269\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSimon Katz, George Chilingar and Leonid Khilyuk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 270\u003c\/p\u003e \u003cp\u003e13.2 Simulated Decline Curves 271\u003c\/p\u003e \u003cp\u003e13.3 Nonlinear Least Squares for Decline Curve Approximation 273\u003c\/p\u003e \u003cp\u003e13.4 New Method of Grid Search for Approximation and Forecast of Decline Curves 273\u003c\/p\u003e \u003cp\u003e13.5 Iterative Minimization of Least Squares with Multiple Approximating Models 275\u003c\/p\u003e \u003cp\u003e13.6 Grid Search Followed by Iterative Minimization with Levenberg-Marquardt Algorithm 276\u003c\/p\u003e \u003cp\u003e13.7 Two Methods for Aggregated Forecast and Analysis of Forecast Uncertainty 277\u003c\/p\u003e \u003cp\u003e13.8 Uncertainty Quantile Ranges Obtained Using Monte Carlo and Bootstrap Methods 279\u003c\/p\u003e \u003cp\u003e13.9 Monte Carlo Forecast and Analysis of Forecast Uncertainty 280\u003c\/p\u003e \u003cp\u003e13.10 Block Bootstrap Forecast and Analysis of Forecast Uncertainty 284\u003c\/p\u003e \u003cp\u003e13.11 Comparative Analysis of Results of Monte Carlo and Bootstrap Simulations 285\u003c\/p\u003e \u003cp\u003e13.12 Conclusions 287\u003c\/p\u003e \u003cp\u003eReferences 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Oil and Gas Company Production, Reserves, and Valuation 289\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMark J. Kaiser\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 290\u003c\/p\u003e \u003cp\u003e14.2 Reserves 292\u003c\/p\u003e \u003cp\u003e14.2.1 Proved Reserves 292\u003c\/p\u003e \u003cp\u003e14.2.2 Proved Reserves Categories 292\u003c\/p\u003e \u003cp\u003e14.2.3 Reserves Reporting 293\u003c\/p\u003e \u003cp\u003e14.2.4 Probable and Possible Reserves 293\u003c\/p\u003e \u003cp\u003e14.2.5 Contractual Differences 294\u003c\/p\u003e \u003cp\u003e14.3 Production 294\u003c\/p\u003e \u003cp\u003e14.4 Factors that Impact Company Value 295\u003c\/p\u003e \u003cp\u003e14.4.1 Ownership 295\u003c\/p\u003e \u003cp\u003e14.4.1.1 International Oil Companies 295\u003c\/p\u003e \u003cp\u003e14.4.1.2 National Oil Companies 296\u003c\/p\u003e \u003cp\u003e14.4.1.3 Government Sponsored Entities 296\u003c\/p\u003e \u003cp\u003e14.4.1.4 Independents and Juniors 297\u003c\/p\u003e \u003cp\u003e14.4.2 Degree of Integration 297\u003c\/p\u003e \u003cp\u003e14.4.3 Product mix 298\u003c\/p\u003e \u003cp\u003e14.4.4 Commodity Price 298\u003c\/p\u003e \u003cp\u003e14.4.5 Production Cost 299\u003c\/p\u003e \u003cp\u003e14.4.6 Finding Cost 299\u003c\/p\u003e \u003cp\u003e14.4.7 Assets 300\u003c\/p\u003e \u003cp\u003e14.4.8 Capital Structure 300\u003c\/p\u003e \u003cp\u003e14.4.9 Geologic Diversification 301\u003c\/p\u003e \u003cp\u003e14.4.10 Geographic Diversification 301\u003c\/p\u003e \u003cp\u003e14.4.11 Unobservable Factors 302\u003c\/p\u003e \u003cp\u003e14.5 Summary Statistics 303\u003c\/p\u003e \u003cp\u003e14.5.1 Sample 303\u003c\/p\u003e \u003cp\u003e14.5.2 Variables 303\u003c\/p\u003e \u003cp\u003e14.5.3 Data Source 305\u003c\/p\u003e \u003cp\u003e14.5.4 International Oil Companies 305\u003c\/p\u003e \u003cp\u003e14.5.5 Independents 308\u003c\/p\u003e \u003cp\u003e14.6 Market Capitalization 309\u003c\/p\u003e \u003cp\u003e14.6.1 Functional Specification 309\u003c\/p\u003e \u003cp\u003e14.6.2 Expectations 309\u003c\/p\u003e \u003cp\u003e14.7 International Oil Companies 310\u003c\/p\u003e \u003cp\u003e14.8 U.S. Independents 312\u003c\/p\u003e \u003cp\u003e14.8.1 Large vs. Small Cap, Oil vs. Gas 312\u003c\/p\u003e \u003cp\u003e14.8.2 Consolidated Small-Caps 314\u003c\/p\u003e \u003cp\u003e14.8.3 Multinational vs. Domestic 314\u003c\/p\u003e \u003cp\u003e14.8.4 Conventional vs. Unconventional 315\u003c\/p\u003e \u003cp\u003e14.8.5 Production and Reserves 316\u003c\/p\u003e \u003cp\u003e14.8.6 Regression Models 316\u003c\/p\u003e \u003cp\u003e14.9 Private Companies 318\u003c\/p\u003e \u003cp\u003e14.10 National Oil Companies of OPEC 320\u003c\/p\u003e \u003cp\u003e14.11 Government Sponsored Enterprises and Other International Companies 320\u003c\/p\u003e \u003cp\u003e14.12 Conclusions 323\u003c\/p\u003e \u003cp\u003eReferences 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: Unconventional Reservoirs 337\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 An Analytical Thermal-Model for Optimization of Gas-Drilling in Unconventional Tight-Sand Reservoirs 339\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBoyun Guo, Gao Li and Jinze Song\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 340\u003c\/p\u003e \u003cp\u003e15.2 Mathematical Model 341\u003c\/p\u003e \u003cp\u003e15.3 Model Comparison 346\u003c\/p\u003e \u003cp\u003e15.4 Sensitivity Analysis 348\u003c\/p\u003e \u003cp\u003e15.5 Model Applications 349\u003c\/p\u003e \u003cp\u003e15.6 Conclusions 351\u003c\/p\u003e \u003cp\u003eNomenclature 352\u003c\/p\u003e \u003cp\u003eAcknowledgements 353\u003c\/p\u003e \u003cp\u003eReferences 353\u003c\/p\u003e \u003cp\u003eAppendix A: Steady Heat Transfer Solution for Fluid Temperature in Counter-Current Flow 355\u003c\/p\u003e \u003cp\u003eAssumptions 355\u003c\/p\u003e \u003cp\u003eGoverning Equation 355\u003c\/p\u003e \u003cp\u003eBoundary Conditions 360\u003c\/p\u003e \u003cp\u003eSolution 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Development of an Analytical Model for Predicting the Fluid Temperature Profile in Drilling Gas Hydrates Reservoirs 363\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLiqun Shan, Boyun Guo and Xiao Cai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 364\u003c\/p\u003e \u003cp\u003e16.2 Mathematical Model 365\u003c\/p\u003e \u003cp\u003e16.3 Case Study 373\u003c\/p\u003e \u003cp\u003e16.4 Sensitivity Analysis 374\u003c\/p\u003e \u003cp\u003e16.5 Conclusions 377\u003c\/p\u003e \u003cp\u003eAcknowledgements 378\u003c\/p\u003e \u003cp\u003eNomenclature 378\u003c\/p\u003e \u003cp\u003eReferences 379\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Distinguishing Between Brine-Saturated and Gas-Saturated Shaly Formations with a Monte-Carlo Simulation of Seismic Velocities 383\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSimon Katz, George Chilingar and Leonid Khilyuk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 384\u003c\/p\u003e \u003cp\u003e17.2 Random Models for Seismic Velocities 385\u003c\/p\u003e \u003cp\u003e17.3 Variability of Seismic Velocities Predicted by Random Models 387\u003c\/p\u003e \u003cp\u003e17.4 The Separability of (\u003ci\u003eV\u003csub\u003ep\u003c\/sub\u003e\u003c\/i\u003e , \u003ci\u003eV\u003csub\u003es\u003c\/sub\u003e\u003c\/i\u003e ) Clusters for Gas- and Brine-Saturated Formations 388\u003c\/p\u003e \u003cp\u003e17.5 Reliability Analysis of Identifying Gas-Filled Formations 389\u003c\/p\u003e \u003cp\u003e17.5.1 Classification with K-Nearest Neighbor 391\u003c\/p\u003e \u003cp\u003e17.5.2 Classification with Recursive Partitioning 392\u003c\/p\u003e \u003cp\u003e17.5.3 Classification with Linear Discriminant Analysis 394\u003c\/p\u003e \u003cp\u003e17.5.4 Comparison of the Three Classification Techniques 395\u003c\/p\u003e \u003cp\u003e17.6 Conclusions 396\u003c\/p\u003e \u003cp\u003eReferences 397\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Shale Mechanical Properties Influence Factors Overview and Experimental Investigation on Water Content Effects 399\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHui Li, Bitao Lai and Shuhua Lin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 400\u003c\/p\u003e \u003cp\u003e18.2 Influence Factors 400\u003c\/p\u003e \u003cp\u003e18.2.1 Effective Pressure 401\u003c\/p\u003e \u003cp\u003e18.2.2 Porosity 402\u003c\/p\u003e \u003cp\u003e18.2.3 Water Content 403\u003c\/p\u003e \u003cp\u003e18.2.4 Salt Solutions 405\u003c\/p\u003e \u003cp\u003e18.2.5 Total Organic Carbon (TOC) 406\u003c\/p\u003e \u003cp\u003e18.2.6 Clay Content 407\u003c\/p\u003e \u003cp\u003e18.2.7 Bedding Plane Orientation 408\u003c\/p\u003e \u003cp\u003e18.2.8 Mineralogy 411\u003c\/p\u003e \u003cp\u003e18.2.9 Anisotropy 413\u003c\/p\u003e \u003cp\u003e18.2.10 Temperature 413\u003c\/p\u003e \u003cp\u003e18.3 Experimental Investigation of Water Saturation Effects on Shale’s Mechanical Properties 414\u003c\/p\u003e \u003cp\u003e18.3.1 Experiment Description 414\u003c\/p\u003e \u003cp\u003e18.3.2 Results and Discussion 414\u003c\/p\u003e \u003cp\u003e18.3.3 Error Analysis of Experiments 417\u003c\/p\u003e \u003cp\u003e18.4 Conclusions 418\u003c\/p\u003e \u003cp\u003eAcknowledgements 420\u003c\/p\u003e \u003cp\u003eReferences 420\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 6: Enhance Oil Recovery 427\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 A Numerical Investigation of Enhanced Oil Recovery Using Hydrophilic Nanofuids 429\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYin Feng, Liyuan Cao and Erxiu Shi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 430\u003c\/p\u003e \u003cp\u003e19.2 Simulation Framework 432\u003c\/p\u003e \u003cp\u003e19.2.1 Background 432\u003c\/p\u003e \u003cp\u003e19.2.2 Two Essential Computational Components 433\u003c\/p\u003e \u003cp\u003e19.2.2.1 Flow Model 433\u003c\/p\u003e \u003cp\u003e19.2.2.2 Nanoparticle Transport and Retention Model 435\u003c\/p\u003e \u003cp\u003e19.3 Coupling of Mathematical Models 437\u003c\/p\u003e \u003cp\u003e19.4 Verification Cases 439\u003c\/p\u003e \u003cp\u003e19.4.1 Effect of Time Steps on the Performance of the in House Simulator 439\u003c\/p\u003e \u003cp\u003e19.4.2 Comparison with Eclipse 440\u003c\/p\u003e \u003cp\u003e19.4.3 Comparison with Software MNM1D 442\u003c\/p\u003e \u003cp\u003e19.5 Results 443\u003c\/p\u003e \u003cp\u003e19.5.1 Continuous Injection 445\u003c\/p\u003e \u003cp\u003e19.5.1.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption 445\u003c\/p\u003e \u003cp\u003e19.5.1.2 Effect of Injection Rate on Oil Recovery and Nanoparticle Adsorption 447\u003c\/p\u003e \u003cp\u003e19.5.2 Slug Injection 449\u003c\/p\u003e \u003cp\u003e19.5.2.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption 449\u003c\/p\u003e \u003cp\u003e19.5.2.2 Effect of Slug Size on Oil Recovery and Nanoparticle Adsorption 451\u003c\/p\u003e \u003cp\u003e19.5.3 Water Postflush 452\u003c\/p\u003e \u003cp\u003e19.5.3.1 Effect of Injection Time Length 452\u003c\/p\u003e \u003cp\u003e19.5.3.2 Effect of Flow Rate Ratio Between Water and Nanofuids on Oil and Nanoparticle Recovery 452\u003c\/p\u003e \u003cp\u003e19.5.4 3D Model Showcase 455\u003c\/p\u003e \u003cp\u003e19.6 Discussions 457\u003c\/p\u003e \u003cp\u003e19.7 Conclusions and Future Work 459\u003c\/p\u003e \u003cp\u003eReferences 461\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 3D Seismic-Assisted CO\u003csub\u003e2\u003c\/sub\u003e -EOR Flow Simulation for the Tensleep Formation at Teapot Dome, USA 463\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePayam Kavousi Ghahfarokhi, Thomas H. Wilson and Alan Lee Brown\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Presentation Sequence 464\u003c\/p\u003e \u003cp\u003e20.2 Introduction 464\u003c\/p\u003e \u003cp\u003e20.3 Geological Background 468\u003c\/p\u003e \u003cp\u003e20.4 Discrete Fracture Network (DFN) 469\u003c\/p\u003e \u003cp\u003e20.5 Petrophysical Modeling 473\u003c\/p\u003e \u003cp\u003e20.6 PVT Analysis 473\u003c\/p\u003e \u003cp\u003e20.7 Streamline Analysis 479\u003c\/p\u003e \u003cp\u003e20.8 Co\u003csub\u003e2\u003c\/sub\u003e -EOR 479\u003c\/p\u003e \u003cp\u003e20.9 Conclusions 483\u003c\/p\u003e \u003cp\u003eAcknowledgement 483\u003c\/p\u003e \u003cp\u003eReferences 484\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 7: New Advances in Reservoir Characterization-Machine Learning Applications 487\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Application of Machine Learning in Reservoir Characterization 489\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFred Aminzadeh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Brief Introduction to Reservoir Characterization 489\u003c\/p\u003e \u003cp\u003e21.2 Artificial Intelligence and Machine (Deep) Learning Review 491\u003c\/p\u003e \u003cp\u003e21.2.1 Support Vector Machines 492\u003c\/p\u003e \u003cp\u003e21.2.2 Clustering (Unsupervised Classification) 492\u003c\/p\u003e \u003cp\u003e21.2.3 Ensemble Methods 497\u003c\/p\u003e \u003cp\u003e21.2.4 Artificial Neural Networks (ANN)- Based Methods 498\u003c\/p\u003e \u003cp\u003e21.3 Artificial Intelligence and Machine (Deep) Learning Applications to Reservoir Characterization 502\u003c\/p\u003e \u003cp\u003e21.3.1 3D Structural Model Development 503\u003c\/p\u003e \u003cp\u003e21.3.2 Sedimentary Modeling 506\u003c\/p\u003e \u003cp\u003e21.3.3 3D Petrophysical Modeling 508\u003c\/p\u003e \u003cp\u003e21.3.4 Dynamic Modeling and Simulations 512\u003c\/p\u003e \u003cp\u003e21.4 Machine (Deep) Learning and Enhanced Oil Recovery (EOR) 513\u003c\/p\u003e \u003cp\u003e21.4.1 ANNs for EOR Performance and Economics 514\u003c\/p\u003e \u003cp\u003e21.4.2 ANNs for EOR Screening 516\u003c\/p\u003e \u003cp\u003e21.5 Conclusion 517\u003c\/p\u003e \u003cp\u003eAcknowledgement 518\u003c\/p\u003e \u003cp\u003eReferences 518\u003c\/p\u003e \u003cp\u003eIndex 525\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407082561879,"sku":"9781119556213","price":164.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119556213.jpg?v=1730498118","url":"https:\/\/bookcurl.com\/products\/reservoir-characterization-9781119556213","provider":"Book Curl","version":"1.0","type":"link"}