Reservoir Characterization. Fundamentals and Applications

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Foreword
Preface
Part 1: Introduction
	1 Reservoir Characterization: Fundamental and Applications An Overview
		1.1 Introduction to Reservoir Characterization?
		1.2 Data Requirements for Reservoir Characterization
		1.3 SURE Challenge
		1.4 Reservoir Characterization in the Exploration, Development and Production Phases
			1.4.1 Exploration Stage/Development Stage
			1.4.2 Primary Production Stage
			1.4.3 Secondary/Tertiary Production Stage
		1.5 Dynamic Reservoir Characterization (DRC)
			1.5.1 4D Seismic for DRC
			1.5.2 Microseismic Data for DRC
		1.6 More on Reservoir Characterization and Reservoir Modeling for Reservoir Simulation
			1.6.1 Rock Physics
			1.6.2 Reservoir Modeling
		1.7 Conclusion
		References
Part 2: General Reservoir Characterization and Anomaly Detection
	2 A Comparison Between Estimated Shear Wave Velocity and Elastic Modulus by Empirical Equations and that of Laboratory Measureme
		2.1 Introduction
		2.2 Methodology
			2.1.2 Estimating the Shear Wave Velocity
			2.2.2 Estimating Geomechanical Parameters
		2.3 Laboratory Set Up and Measurements
			2.3.1 Laboratory Data Collection
		2.4 Results and Discussion
		2.5 Conclusions
		2.6 Acknowledgment
		References
	3 Anomaly Detection within Homogenous Geologic Area
		3.1 Introduction
		3.2 Anomaly Detection Methodology
		3.3 Basic Anomaly Detection Classifiers
		3.4 Prior and Posterior Characteristics of Anomaly Detection Performance
		3.5 ROC Curve Analysis
		3.6 Optimization of Aggregated AD Classifier Using Part of the Anomaly Identified by Universal Classifiers
		3.7 Bootstrap Based Tests of Anomaly Type Hypothesis
		3.8 Conclusion
		References
	4 Characterization of Carbonate Source-Derived Hydrocarbons Using Advanced Geochemical Technologies
		4.1 Introduction
		4.2 Samples and Analyses Performed
		4.3 Results and Discussions
		4.4 Summary and Conclusions
		References
	5 Strategies in High-Data-Rate MWD Mud Pulse Telemetry
		5.1 Summary
			5.1.1 High Data Rates and Energy Sustainability
			5.1.2 Introduction
			5.1.3 MWD Telemetry Basics
			5.1.4 New Telemetry Approach
		5.2 New Technology Elements
			5.2.1 Downhole Source and Signal Optimization
			5.2.2 Surface Signal Processing and Noise Removal
			5.2.3 Pressure, Torque and Erosion Computer Modeling
			5.2.4 Wind Tunnel Analysis: Studying New Approaches
			5.2.5 Example Test Results
		5.3 Directional Wave Filtering
			5.3.1 Background Remarks
			5.3.2 Theory
			5.3.3 Calculations
		5.4 Conclusions
		Acknowledgments
		References
	6 Detection of Geologic Anomalies with Monte Carlo Clustering Assemblies
		6.1 Introduction
		6.2 Analysis of Inhomogeneity of the Training and Test Sets and Instability of Clustering
		6.3 Formation of Multiple Randomized Test Sets and Construction of the Clustering Assemblies
		6.4 Irregularity Index of Individual Clusters in the Cluster Set
		6.5 Anomaly Indexes of Individual Records and Clustering Assemblies
		6.6 Prior and Posterior True and False Discovery Rates for Anomalous and Regular Records
		6.7 Estimates of Prior False Discovery Rates for Anomalous Cluster Sets, Clusters, and Individual Records. Permeability Dataset
		6.8 Posterior Analysis of Efficiency of Anomaly Identification. High Permeability Anomaly
		6.9 Identification of Records in the Gas Sand Dataset as Anomalous, using Brine Sand Dataset as Data with Regular Records
		6.10 Notations
		6.11 Conclusions
		References
	7 Dissimilarity Analysis of Petrophysical Parameters as Gas-Sand Predictors
		7.1 Introduction
		7.2 Petrophysical Parameters for Gas-Sand Identification
		7.3 Lithologic and Fluid Content Dissimilarities of Values of Petrophysical Parameters
		7.4 Parameter Ranking and Efficiency of Identification of Gas-Sands
		7.5 ROC Curve Analysis with Cross Validation
		7.6 Ranking Parameters According to AUC Values
		7.7 Classification with Multidimensional Parameters as Gas Predictors
		7.8 Conclusions
		Definitions and Notations
		References
	8 Use of Type Curve for Analyzing Non-Newtonian Fluid Flow Tests Distorted by Wellbore Storage Effects
		8.1 Introduction
		8.2 Objective
		8.3 Problem Analysis
			8.3.1 Model Assumptions
			8.3.2 Solution Without the Wellbore Storage Distortion
			8.3.3 Wellbore Storage and Skin Effects
			8.3.4 Solution by Mathematical Inspection
			8.3.5 Solution Verification
		8.4 Use of Finite Element
		8.5 Analysis Methodology
			8.5.1 Finding the
			8.5.2 Dimensionless Wellbore Storage
			8.5.3 Use of Type Curves
			8.5.4 Match Point
			8.5.5 Uncertainty in Analysis
		8.6 Test Data Examples
			8.6.1 Match Point
			8.6.2 Match Point
			8.6.3 Analysis Recommendations
			8.6.4 Match Point
			8.6.5 Analysis Recommendations
			8.6.6 Match Point
		8.7 Conclusion
		Nomenclature
		References
		Appendix A: Non-Linear Boundary Condition and Laplace Transform
		Appendix B: Type Curve Charts for Various Power Law Indices
Part 3: Reservoir Permeability Detection
	9 Permeability Prediction Using Machine Learning, Exponential, Multiplicative, and Hybrid Models
		9.1 Introduction
		9.2 Additive, Multiplicative, Exponential, and Hybrid Permeability Models
		9.3 Combination of Basis Function Expansion and Exhaustive Search for Optimum Subset of Predictors
		9.4 Outliers in the Forecasts Produced with Four Permeability Models
		9.5 Additive, Multiplicative, and Exponential Committee Machines
		9.6 Permeability Forecast with First Level Committee Machines. Sandstone Dataset
		9.7 Permeability Prediction with First Level Committee Machines. Carbonate Reservoirs
		9.8 Analysis of Accuracy of Outlier Replacement by The First and Second Level Committee Machines. Sandstone Dataset
		9.9 Conclusion
		References
	10 Geological and Geophysical Criteria for Identifying Zones of High Gas Permeability of Coals (Using the Example of Kuzbass CBM
		10.1 Introduction
		10.2 Physical Properties and External Load Conditions on a Coal Reservoir
		10.3 Basis for Evaluating Physical and Mechanical Coalbed Properties in the Borehole Environment
		10.4 Conclusions
		Acknowledgement
		References
	11 Rock Permeability Forecasts Using Machine Learning and Monte Carlo Committee Machines
		11.1 Introduction
		11.2 Monte Carlo Cross Validation and Monte Carlo Committee Machines
		11.3 Performance of Extended MC Cross Validation and Construction MC Committee Machines
		11.4 Parameters of Distribution of the Number of Individual Forecasts in Monte Carlo Cross Validation
		11.5 Linear Regression Permeability Forecast with Empirical Permeability Models
		11.6 Accuracy of the Forecasts with Machine Learning Methods
		11.7 Analysis of Instability of the Forecast
		11.8 Enhancement of Stability of the MC Committee Machines Forecast Via Increase of the Number of Individual Forecasts
		11.9 Conclusions
		Nomenclature
		Appendix 1Description of Permeability Models from Different Fields
		Appendix 2A Brief Overview of Modular Networks or Committee Machines*
		References
Part 4: Reserves Evaluation/Decision Making
	12 The Gulf of Mexico Petroleum System – Foundation for Science-Based Decision Making
		Introduction
		Basin Development and Geologic Overview
		Petroleum System
		Reservoir Geology
		Hydrocarbons
		Salt and Structure
		Conclusions
		Acknowledgments and Disclaimer
		References
	13 Forecast and Uncertainty Analysis of Production Decline Trends with Bootstrap and Monte Carlo Modeling
		13.1 Introduction
		13.2 Simulated Decline Curves
		13.3 Nonlinear Least Squares for Decline Curve Approximation
		13.4 New Method of Grid Search for Approximation and Forecast of Decline Curves
		13.5 Iterative Minimization of Least Squares with Multiple Approximating Models
		13.6 Grid Search Followed by Iterative Minimization with Levenberg-Marquardt Algorithm
		13.7 Two Methods for Aggregated Forecast and Analysis of Forecast Uncertainty
		13.8 Uncertainty Quantile Ranges Obtained Using Monte Carlo and Bootstrap Methods
		13.9 Monte Carlo Forecast and Analysis of Forecast Uncertainty
		13.10 Block Bootstrap Forecast and Analysis of Forecast Uncertainty
		13.11 Comparative Analysis of Results of Monte Carlo and Bootstrap Simulations
		13.12 Conclusions
		References
	14 Oil and Gas Company Production, Reserves, and Valuation
		14.1 Introduction
		14.2 Reserves
			14.2.1 Proved Reserves
			14 .2.2 Proved Reserves Categories
			14.2.3 Reserves Reporting
			14.2.4 Probable and Possible Reserves
			14.2.5 Contractual Differences
		14.3 Production
		14.4 Factors that Impact Company Value
			14.4.1 Ownership
				14.4.1.1 International Oil Companies
				14.4.1.2 National Oil Companies
				14.4.1.3 Government Sponsored Entities
				14.4.1.4 Independents and Juniors
			14.4.2 Degree of Integration
			14.4.3 Product Mix
			14.4.4 Commodity Price
			14.4.5 Production Cost
			14.4.6 Finding Cost
			14.4.7 Assets
			14.4.8 Capital Structure
			14.4.9 Geologic Diversification
			14.4.10 Geographic Diversification
			14.4.11 Unobservable Factors
		14.5 Summary Statistics
			14.5.1 Sample
			14.5.2 Variables
			14.5.3 Data Source
			14.5.4 International Oil Companies
			14.5.5 Independents
		14.6 Market Capitalization
			14.6.1 Functional Specification
			14.6.2 Expectations
		14.7 International Oil Companies
		14.8 U.S. Independents
			14.8.1 Large vs. Small Cap, Oil vs. Gas
			14.8.2 Consolidated Small-Caps
			14.8.3 Multinational vs. Domestic
			14.8.4 Conventional vs. Unconventional
			14.8.5 Production and Reserves
			14.8.6 Regression Models
		14.9 Private Companies
		14.10 National Oil Companies of OPEC
		14.11 Government Sponsored Enterprises and Other International Companies
		14.12 Conclusions
		References
Part 5: Unconventional Reservoirs
	15 An Analytical Thermal-Model for Optimization of Gas-Drilling in Unconventional Tight-Sand Reservoirs
		15.1 Introduction
		15.2 Mathematical Model
		15.3 Model Comparison
		15.4 Sensitivity Analysis
		15.5 Model Applications
		15.6 Conclusions
		Acknowledgements
		References
		Appendix A: Steady Heat Transfer Solution for Fluid Temperature in Counter-Current Flow
			Assumptions
			Governing Equation
	16 Development of an Analytical Model for Predicting the Fluid Temperature Profile in Drilling Gas Hydrates Reservoirs
		16.1 Introduction
		16.2 Mathematical Model
		16.3 Case Study
		16.4 Sensitivity Analysis
		16.5 Conclusions
		Acknowledgements
		Nomenclature
		References
	17 Distinguishing Between BrineSaturated and Gas-Saturated Shaly Formations with a Monte-Carlo Simulation of Seismic Velocities
		17.1 Introduction
		17.2 Random Models for Seismic Velocities
		17.3 Variability of Seismic Velocities Predicted by Random Models
		17.4 The Separability of (Vp, Vs) Clusters for Gas- and Brine-Saturated Formations
		17.5 Reliability Analysis of Identifying Gas-Filled Formations
			17.5.1 Classification with K-Nearest Neighbor
			17.5.2 Classification with Recursive Partitioning
			17.5.3 Classification with Linear Discriminant Analysis
			17.5.4 Comparison of the Three Classification Techniques
		17.6 Conclusions
		References
	18 Shale Mechanical Properties Influence Factors Overview and Experimental Investigation on Water Content Effects
		18.1 Introduction
		18.2 Influence Factors
			18.2.1 Effective Pressure
			18.2.2 Porosity
			18.2.3 Water Content
			18.2.4 Salt Solutions
			18.2.5 Total Organic Carbon (TOC)
			18.2.6 Clay Content
			18.2.7 Bedding Plane Orientation
			18.2.8 Mineralogy
			18.2.9 Anisotropy
			18.2.10 Temperature
		18.3 Experimental Investigation of Water Saturation Effects on Shale’s Mechanical Properties
			18.3.1 Experiment Description
			18.3.2 Results and Discussion
			18.3.3 Error Analysis of Experiments
		18.4 Conclusions
		Acknowledgements
		References
Part 6: Enhance Oil Recovery
	19 A Numerical Investigation of Enhanced Oil Recovery Using Hydrophilic Nanofuids
		19.1 Introduction
		19.2 Simulation Framework
			19.2.1 Background
			19.2.2 Two Essential Computational Components
				19.2.2.1 Flow Model
				19.2.2.2 Nanoparticle Transport and Retention Model
		19.3 Coupling of Mathematical Models
		19.4 Verification Cases
			19.4.1 Effect of Time Steps on the Performance of the in House Simulator
			19.4.2 Comparison with Eclipse
			19.4.3 Comparison with Software MNM1D
		19.5 Results
			19.5.1 Continuous Injection
				19.5.1.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption
				19.5.1.2 Effect of Injection Rate on Oil Recovery and Nanoparticle Adsorption
			19.5.2 Slug Injection
				19.5.2.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption
				19.5.2.2 Effect of Slug Size on Oil Recovery and Nanoparticle Adsorption
			19.5.3 Water Postflush
				19.5.3.1 Effect of Injection Time Length
				19.5.3.2 Effect of Flow Rate Ratio Between Water and Nanofuids on Oil and Nanoparticle Recovery
			19.5.4 3D Model Showcase
		19.6 Discussions
		19.7 Conclusions and Future Work
		References
	20 3D Seismic-Assisted CO2-EOR Flow Simulation for the Tensleep Formation at Teapot Dome, USA
		20.1 Presentation Sequence
		20.2 Introduction
		20.3 Geological Background
		20.4 Discrete Fracture Network (DFN)
		20.5 Petrophysical Modeling
		20.6 PVT Analysis
		20.7 Streamline Analysis
		20.8 CO2-EOR
		20.9 Conclusions
		Acknowledgement
		References
Part 7: New Advances in Reservoir Characterization-Machine Learning Applications
	21 Application of Machine Learning in Reservoir Characterization
		21.1 Brief Introduction to Reservoir Characterization
		21.2 Artificial Intelligence and Machine (Deep) Learning Review
			21.2.1 Support Vector Machines
			21.2.2 Clustering (Unsupervised Classification)
			21.2.3 Ensemble Methods
			21.2.4 Artificial Neural Networks (ANN)-Based Methods
		21.3 Artificial Intelligence and Machine (Deep) Learning Applications to Reservoir Characterization
			21.3.1 3D Structural Model Development
			21.3.2 Sedimentary Modeling
			21.3.3 3D Petrophysical Modeling
			21.3.4 Dynamic Modeling and Simulations
		21.4 Machine (Deep) Learning and Enhanced Oil Recovery (EOR)
			21.4.1 ANNs for EOR Performance and Economics
			21.4.2 ANNs for EOR Screening
		21.5 Conclusion
		Acknowledgement
		References
Index
Also of Interest
EULA