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El. knyga: Petroleum Reservoir Modeling and Simulation: Geology, Geostatistics, and Performance Prediction

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  • Formatas: 464 pages
  • Išleidimo metai: 28-Jan-2022
  • Leidėjas: McGraw-Hill Education
  • Kalba: eng
  • ISBN-13: 9781259834301
Kitos knygos pagal šią temą:
Petroleum Reservoir Modeling and Simulation: Geology, Geostatistics, and Performance PredictionDidesnis paveikslėlis
  • Formatas: 464 pages
  • Išleidimo metai: 28-Jan-2022
  • Leidėjas: McGraw-Hill Education
  • Kalba: eng
  • ISBN-13: 9781259834301
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Detailed reservoir engineering fundamentals and real-world applications along with well testing procedures 

This practical resource provides you with the tools and techniques you need to successfully construct petroleum reservoir models of all types and sizes. You will learn how to improve reserve estimations and make development decisions that will optimize well performance. Each chapter features detailed explanations and applications as well as examples and exercise questions that reinforce salient points.

Petroleum Reservoir Simulation and Modeling: Geology, Geostatistics, and Performance Prediction describes the process of applying reservoir modeling techniques and flow analysis methods to specific geologic systems encountered in all subsurface exploration and development. Special attention is given to shale, carbonate, and subsea formations. You will get comprehensive coverage of geologic descriptions, quantitative modeling, geostatistics, well testing principles, upscaled models, and history matching. 

•Contains worked-out numerical examples and cases studies
•Provides software simulation modules that demonstrate modeling and analysis
•Written by a team of experienced engineers and academics


1 Introduction 1(6)
1.1 Introductory Concepts
3(3)
1.1.1 Stochastic Reservoir Modeling
3(1)
1.1.2 Flow Modeling in Highly Heterogeneous Reservoirs
4(1)
1.1.3 Linking Geostatistical Modeling to Flow Modeling
5(1)
1.2 Reference
6(1)
2 Spatial Correlation 7(50)
2.1 Spatial Covariance
8(1)
2.2 Semi-Variogram
9(12)
2.2.1 Variogram Inference
11(1)
2.2.2 Variogram Characteristics
12(2)
2.2.3 Pure Nugget Effect
14(1)
2.2.4 Behavior Next to the Origin
15(3)
2.2.5 Outliers and Semi-Variogram Inference
18(2)
2.2.6 Proportion Effect
20(1)
2.3 Variogram Modeling
21(34)
2.3.1 Positive Definiteness
22(2)
2.3.2 Allowable Linear Combinations
24(1)
2.3.3 Legitimate Structural Models
25(8)
2.3.4 Positive Combinations
33(3)
2.3.5 Geometric Anisotropy
36(2)
2.3.6 Coordinate Transform in 2D
38(7)
2.3.7 Modeling Anisotropy in 3D
45(2)
2.3.8 Semi-Variogram Computation in 3D
47(4)
2.3.9 Zonal Anisotropy
51(1)
2.3.10 Modeling Strategy
52(1)
2.3.11 3D Model
53(2)
2.4 References
55(2)
3 Spatial Estimation 57(82)
3.1 Linear Least Squares Estimation or Interpolation
57(1)
3.2 Linear Regression
57(6)
3.2.1 Linear Least Squares-An Interpretation
58(2)
3.2.2 Application to Spatial Estimation
60(3)
3.3 Estimation in General
63(5)
3.3.1 Loss Function-Some Analytical Results
65(3)
3.4 Kriging
68(14)
3.4.1 Expected Value of the Error Distribution
70(1)
3.4.2 Error Variance
71(4)
3.4.3 An Example
75(7)
3.5 Universal Kriging
82(17)
3.5.1 Kriging with a Trend Function
83(5)
3.5.2 Ordinary Kriging
88(6)
3.5.3 Universal Kriging Estimate for Trend
94(5)
3.6 Kriging with an External Drift
99(4)
3.7 Indicator Kriging
103(13)
3.7.1 Non-Parametric Approach to Modeling Distributions
103(1)
3.7.2 Kriging in Terms of Projections
104(3)
3.7.3 Indicator Basis Function
107(2)
3.7.4 Indicator Kriging
109(7)
3.8 Data Integration in Kriging
116(21)
3.8.1 Simple Co-Kriging Estimator
118(2)
3.8.2 Simplified Models for Data Integration
120(5)
3.8.3 Linear Model of Coregionalization
125(12)
3.9 References
137(2)
4 Spatial Simulation 139(56)
4.1 Introduction
139(1)
4.2 Kriging-Limitations
140(2)
4.3 Stochastic Simulation
142(16)
4.3.1 Lower-Upper (LU) Simulation
144(8)
4.3.2 Sequential Simulation
152(6)
4.4 Non-Parametric Sequential Simulation
158(22)
4.4.1 Interpolating within the Range of Thresholds Specified
160(1)
4.4.2 Tail Extrapolation
161(4)
4.4.3 Data Integration within the Indicator Framework
165(10)
4.4.4 Markov-Bayes Approach
175(5)
4.5 Data Integration Using the Permanence of Ratio Hypothesis
180(13)
4.5.1 The Tau Model
185(6)
4.5.2 The Nu Model
191(2)
4.6 References
193(2)
5 Geostatistical Simulation Constrained to Higher-Order Statistics 195(44)
5.1 Indicator Basis Function
196(40)
5.1.1 Establishing the Basis Function-Projection Theorem
198(4)
5.1.2 Single Extended Normal Equation
202(1)
5.1.3 Single Normal Equation Simulation
203(21)
5.1.4 Returning to the Full Indicator Basis Function
224(12)
5.2 References
236(3)
6 Numerical Schemes for Flow Simulation 239(46)
6.1 Governing Equations
239(2)
6.1.1 Conservation of Mass
239(1)
6.1.2 Conservation of Momentum
240(1)
6.2 Single-Phase Flow
241(18)
6.2.1 Simulation Equations
241(2)
6.2.2 External Boundary Conditions
243(1)
6.2.3 Initialization
244(1)
6.2.4 Well Models
244(3)
6.2.5 Linearization
247(5)
6.2.6 Solution Methods
252(7)
6.3 Multi-Phase Flow
259(11)
6.3.1 Simulation Equations
259(2)
6.3.2 External Boundary Conditions
261(1)
6.3.3 Initialization
261(1)
6.3.4 Well Models
262(1)
6.3.5 Linearization and Solution Methods
263(7)
6.4 Finite Element Formulation
270(5)
6.5 Solution of Linear System of Equations
275(6)
6.6 References
281(4)
7 Gridding Schemes for Flow Simulation 285(48)
7.1 Gridding Schemes
285(6)
7.1.1 Overview
285(1)
7.1.2 Cartesian Grid
286(1)
7.1.3 Corner-Point Grid
286(1)
7.1.4 Perpendicular Bisector Grid
287(2)
7.1.5 General Unstructured Grid
289(1)
7.1.6 Other Specialized Gridding Options
290(1)
7.2 Consistency, Stability, and Convergence
291(14)
7.2.1 Consistency
291(7)
7.2.2 Stability
298(7)
7.2.3 Convergence
305(1)
7.3 Advanced Numerical Schemes for Unstructured Grids
305(13)
7.3.1 Generalized CVFD-TPFA Formulation
305(1)
7.3.2 CVFD-MPFA Formulation
306(5)
7.3.3 Control Volume Finite Element Formulation
311(3)
7.3.4 Mixed Finite Element Formulation
314(4)
7.4 Dual Media Models
318(11)
7.4.1 Dual-Permeability Formulation
318(3)
7.4.2 Dual-Porosity Formulation
321(1)
7.4.3 Embedded Discrete Fracture Model
322(7)
7.5 References
329(4)
8 Upscaling of Reservoir Models 333(48)
8.1 Statistical Upscaling
333(6)
8.1.1 Power Average
333(1)
8.1.2 Statistical Re-Normalization
334(2)
8.1.3 Facies Upscaling
336(3)
8.2 Flow-Based Upscaling
339(13)
8.2.1 Effective Medium Approximations
339(1)
8.2.2 Single-Phase Flow
339(8)
8.2.3 Two-Phase Flow (Relative Permeability)
347(4)
8.2.4 Compositional Flow Simulation
351(1)
8.3 Scale-Up
352(8)
8.3.1 General Concepts
353(4)
8.3.2 Scale-Up of Linearly Averaged Attributes
357(1)
8.3.3 Scale-Up of Non-Linearly Averaged Attributes
358(2)
8.4 Scale-Up of Flow and Transport Equations
360(16)
8.4.1 Dimensionless Scaling Groups
360(1)
8.4.2 Stochastic Perturbation Methods
361(6)
8.4.3 Volume Averaging Methods
367(9)
8.5 Final Remarks
376(1)
8.6 References
376(5)
9 History Matching-Dynamic Data Integration 381(48)
9.1 History Matching as an Inverse Problem
382(2)
9.2 Optimization Schemes
384(11)
9.2.1 Gradient-Based Methods
384(2)
9.2.2 Global Methods
386(9)
9.3 Probabilistic Schemes
395(13)
9.3.1 Optimization-Based Bayesian Methods
395(5)
9.3.2 Sampling Algorithms
400(8)
9.4 Ensemble-Based Schemes
408(14)
9.4.1 Ensemble Kalman Filters
408(7)
9.4.2 Ensemble Pattern Search and Model Selection
415(7)
9.5 References
422(7)
A Quantile Variograms 429(6)
B Some Details about the Markov-Bayes Model 435(6)
Index 441
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