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Advanced Petroleum Reservoir Simulation: Towards Developing Reservoir Emulators 2nd edition [Kõva köide]

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  • Formaat: Hardback, 592 pages, kõrgus x laius x paksus: 236x163x36 mm, kaal: 894 g
  • Sari: Wiley-Scrivener
  • Ilmumisaeg: 02-Sep-2016
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119038510
  • ISBN-13: 9781119038511
Teised raamatud teemal:
Advanced Petroleum Reservoir Simulation: Towards Developing Reservoir Emulators 2nd editionSuurem pilt
  • Formaat: Hardback, 592 pages, kõrgus x laius x paksus: 236x163x36 mm, kaal: 894 g
  • Sari: Wiley-Scrivener
  • Ilmumisaeg: 02-Sep-2016
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119038510
  • ISBN-13: 9781119038511
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119038511.html
Märksõnad:

This second edition of the original volume adds significant new innovations for revolutionizing the processes and methods used in petroleum reservoir simulations. With the advent of shale drilling, hydraulic fracturing, and underbalanced drilling has come a virtual renaissance of scientific methodologies in the oil and gas industry.  New ways of thinking are being pioneered, and Dr. Islam and his team have, for years now, been at the forefront of these important changes. 

 This book clarifies the underlying mathematics and physics behind reservoir simulation and makes it easy to have a range of simulation results along with their respective probability.  This makes the risk analysis based on knowledge rather than guess work.  The book offers by far the strongest tool for engineers and managers to back up reservoir simulation predictions with real science.  The book adds transparency and ease to the process of reservoir simulation in way never witnessed before. Finally, No other book provides readers complete access to the 3D, 3-phase reservoir simulation software that is available with this text.

 A must-have for any reservoir engineer or petroleum engineer working upstream, whether in exploration, drilling, or production, this text is also a valuable textbook for advanced students and graduate students in petroleum or chemical engineering departments.

Preface xv
1 Introduction 1(6)
1.1 Summary
1(1)
1.2 Opening Remarks
2(1)
1.3 The Need for a Knowledge-Based Approach
2(3)
1.4 Summary of
Chapters
5(2)
2 Reservoir Simulation Background 7(32)
2.1 Essence of Reservoir Simulation
8(2)
2.2 Assumptions Behind Various Modeling Approaches
10(9)
2.2.1 Material Balance Equation
11(1)
2.2.2 Decline Curve
12(1)
2.2.3 Statistical Method
13(2)
2.2.4 Analytical Methods
15(1)
2.2.5 Finite-Difference Methods
16(3)
2.2.6 Darcy's Law
19(1)
2.3 Recent Advances in Reservoir Simulation
19(12)
2.3.1 Speed and Accuracy
19(2)
2.3.2 New Fluid-Flow Equations
21(5)
2.3.3 Coupled Fluid Flow and Geo-Mechanical Stress Model
26(3)
2.3.4 Fluid-Flow Modeling Under Thermal Stress
29(2)
2.4 Memory Models
31(2)
2.4.1 Thermal Hysteresis
32(1)
2.4.2 Mathematical and Numerical Models
32(1)
2.5 Future Challenges in Reservoir Simulation
33(6)
2.5.1 Experimental Challenges
33(2)
2.5.2 Numerical Challenges
35(10)
2.5.2.1 Theory of Onset and Propagation of Fractures due to Thermal Stress
35(1)
2.5.2.2 Viscous Fingering during Miscible Displacement
36(3)
3 Reservoir Simulator-Input/Output 39(46)
3.1 Input and Output Data
40(2)
3.2 Geological and Geophysical Modeling
42(3)
3.3 Reservoir Characterization
45(13)
3.3.1 Representative Elementary Volume, REV
46(3)
3.3.2 Fluid and Rock Properties
49(5)
3.3.2.1 Fluid Properties
49(5)
3.3.3 Rock Properties
54(4)
3.4 Upscaling
58(7)
3.4.1 Power Law Averaging Method
59(1)
3.4.2 Pressure-Solver Method
60(2)
3.4.3 Renormalization Technique
62(1)
3.4.4 Multiphase Flow Upscaling
63(2)
3.5 Pressure/Production Data
65(1)
3.6 Phase Saturations Distribution
66(2)
3.7 Reservoir Simulator Output
68(2)
3.8 History Matching
70(11)
3.8.1 History-Matching Formulation
72(3)
3.8.2 Uncertainty Analysis
75(12)
3.8.2.1 Measurement Uncertainty
76(2)
3.8.2.2 Upscaling Uncertainty
78(1)
3.8.2.3 Model Error
79(1)
3.8.2.4 The Prediction Uncertainty
80(1)
3.9 Real-Time Monitoring
81(4)
4 Reservoir Simulators: Problems, Shortcomings, and Some Solution Techniques 85(32)
4.1 Multiple Solutions in Natural Phenomena
87(17)
4.1.1 Knowledge Dimension
90(14)
4.2 Adomian Decomposition
104(10)
4.2.1 Governing Equations
106(2)
4.2.2 Adomian Decomposition of Buckley-Leverett Equation
108(3)
4.2.3 Results and Discussions
111(3)
4.3 Some Remarks on Multiple Solutions
114(3)
5 Mathematical Formulation of Reservoir Simulation Problems 117(38)
5.1 Black Oil Model and Compositional Model
119(1)
5.2 General Purpose Compositional Model
120(21)
5.2.1 Basic Definitions
120(2)
5.2.2 Primary and Secondary Parameters and Model Variables
122(3)
5.2.3 Mass Conservation Equation
125(3)
5.2.4 Energy Balance Equation
128(5)
5.2.5 Volume Balance Equation
133(1)
5.2.6 The Motion Equation in Porous Medium
134(5)
5.2.7 The Compositional System of Equations and Model Variables
139(2)
5.3 Simplification of the General Compositional Model
141(5)
5.3.1 The Black Oil Model
141(2)
5.3.2 The Water Oil Model
143(3)
5.4 Some Examples in Application of the General Compositional Model
146(9)
5.4.1 Isothermal Volatile Oil Reservoir
146(2)
5.4.2 Steam Injection Inside a Dead Oil Reservoir
148(2)
5.4.3 Steam Injection in Presence of Distillation and Solution Gas
150(5)
6 The Compositional Simulator Using Engineering Approach 155(84)
6.1 Finite Control Volume Method
156(14)
6.1.1 Reservoir Discretization in Rectangular Coordinates
157(1)
6.1.2 Discretization of Governing Equations
158(10)
6.1.2.1 Components Mass Conservation Equation
158(8)
6.1.2.2 Energy Balance Equation
166(2)
6.1.3 Discretization of Motion Equation
168(2)
6.2 Uniform Temperature Reservoir Compositional Flow Equations in a 1-D Domain
170(5)
6.3 Compositional Mass Balance Equation in a Multidimensional Domain
175(15)
6.3.1 Implicit Formulation of Compositional Model in Multidimensional Domain
178(2)
6.3.2 Reduced Equations of Implicit Compositional Model in Multidimensional Domain
180(3)
6.3.3 Well Production and Injection Rate Terms
183(3)
6.3.3.1 Production Wells
183(2)
6.3.3.2 Injection Wells
185(1)
6.3.4 Fictitious Well Rate Terms (Treatment of Boundary Conditions)
186(4)
6.4 Variable Temperature Reservoir Compositional Flow Equations
190(7)
6.4.1 Energy Balance Equation
190(4)
6.4.2 Implicit Formulation of Variable Temperature Reservoir Compositional Flow Equations
194(3)
6.5 Solution Method
197(6)
6.5.1 Solution of Model Equations Using Newton's Iteration
198(5)
6.6 The Effects of Linearization
203(36)
6.6.1 Case 1: Single Phase Flow of a Natural Gas
203(7)
6.6.2 Effect of Interpolation Functions and Formulation
210(1)
6.6.3 Effect of Time Interval
210(2)
6.6.4 Effect of Permeability
212(2)
6.6.5 Effect of Number of Gridblocks
214(1)
6.6.6 Spatial and Transient Pressure Distribution Using Different Interpolation Functions
214(4)
6.6.7 CPU Time
218(2)
6.6.8 Case 2: An Oil/water Reservoir
220(19)
7 Development of a New Material Balance Equation for Oil Recovery 239(32)
7.1 Summary
239(2)
7.2 Introduction
241(2)
7.3 Mathematical Model Development
243(1)
7.3.1 Permeability Alteration
243(1)
7.3 Porosity Alteration
244(2)
7.4 Pore Volume Change
246(4)
7.4.1 A Comprehensive MBE with Memory for Cumulative Oil Recovery
247(3)
7.5 Numerical Simulation
250(8)
7.5.1 Effects of Compressibilities on Dimensionless Parameters
251(1)
7.4.2 Comparison of Dimensionless Parameters Based on Compressibility Factor
252(1)
7.4.3 Effects of M on Dimensionless Parameter
253(2)
7.4.4 Effects of Compressibility Factor with M Values
255(1)
7.4.5 Comparison of Models Based on RF
255(2)
7.4.6 Effects of M on MBE
257(1)
7.5 Conclusions
258(1)
Appendix
Chapter 7: Development of an MBE for a Compressible Undersaturated Oil Reservoir
259(12)
8 State-of-the-art on Memory Formalism for Porous Media Applications 271(30)
8.1 Summary
271(1)
8.2 Introduction
272(1)
8.3 Historical Development of Memory Concept
273(4)
8.3.1 Constitutive Equations
274(1)
8.3.2 Application of Memory in Diffusion in Porous Media
274(3)
8.3.3 Definition of Memory
277(1)
8.4 State-of-the-art Memory-Based Models
277(7)
8.5 Basset Force: A History Term
284(3)
8.6 Anomalous Diffusion: A memory Application
287(10)
8.6.1 Fractional Order Transport Equations and Numerical Schemes
288(9)
8.7 Future Trends
297(1)
8.8 Conclusion
298(3)
9 Modeling Viscous Fingering During Miscible Displacement in a Reservoir 301(58)
9.1 Improvement of the Numerical Scheme
302(15)
9.1.1 The Governing Equation
303(2)
9.1.2 Finite Difference Approximations
305(2)
9.1.2.1 Barakat-Clark FTD Scheme
305(2)
9.1.2.2 DuFort-Frankel Scheme
307(1)
9.1.3 Proposed Barakat-Clark CTD Scheme
307(2)
9.1.4 Accuracy and Truncation Errors
309(1)
9.1.5 Some Results and Discussion
309(7)
9.1.6 Influence of Boundary Conditions
316(1)
9.2 Application of the New Numerical Scheme to Viscous Fingering
317(42)
9.2.1 Stability Criterion and Onset of Fingering
318(1)
9.2.2 Base Stable Case
318(6)
9.2.3 Base Unstable Case
324(6)
9.2.4 Parametric Study
330(20)
9.2.4.1 Effect of Injection Pressure
331(4)
9.2.4.2 Effect of Overall Porosity
335(1)
9.2.4.3 Effect of Mobility Ratio
336(5)
9.2.4.4 Effect of Longitudinal Dispersion
341(2)
9.2.4.5 Effect of Transverse Dispersion
343(4)
9.2.4.6 Effect of Aspect Ratio
347(3)
9.2.5 Comparison of Numerical Modeling Results with Experimental Results
350(14)
9.2.5.1 Selected Experimental Model
350(1)
9.2.5.2 Physical Model Parameters
350(1)
9.2.5.3 Comparative Study
351(4)
9.2.5.4 Concluding Remarks
355(4)
10 An Implicit Finite-Difference Approximation of Memory-Based Flow Equation in Porous Media 359(24)
10.1 Summary
359(1)
10.2 Introduction
360(1)
10.3 Background
361(3)
10.4 Theoretical Development
364(5)
10.4.1 Mass Conservation
365(1)
10.4.2 Composite Variable, η
366(1)
10.4.3 Implicit Formulation
367(2)
10.6 Numerical Simulation
369(1)
10.7 Results and Discussion
370(11)
10.8 Conclusion
381(2)
11 Towards Modeling Knowledge and Sustainable Petroleum Production 383(82)
11.1 Essence of Knowledge, Science, and Emulation
384(13)
11.1.1 Simulation vs. Emulation
384(2)
11.1.2 Importance of the First Premise and Scientific Pathway
386(2)
11.1.3 Mathematical Requirements of Nature Science
388(4)
11.1.4 The Meaningful Addition
392(2)
11.1.5 "Natural" Numbers and the Mathematical Content of Nature
394(3)
11.2 The Knowledge Dimension
397(3)
11.2.1 The Importance of Time as the Fourth Dimension
398(2)
11.3 Aphenomenal Theories of Modern Era
400(12)
11.3.1 Examples of Linearization and Linear Thinking
408(1)
11.3.2 The Knowledge-Based Cognition Process
409(3)
11.4 Towards Modeling Truth and Knowledge
412(1)
11.5 The Single-Parameter Criterion
413(9)
11.5.1 Science Behind Sustainable Technology
413(2)
11.5.2 A New Computational Method
415(5)
11.5.3 Towards Achieving Multiple Solutions
420(2)
11.6 The Conservation of Mass and Energy
422(20)
11.6.1 The Avalanche Theory
423(5)
11.6.2 Aims of Modeling Natural Phenomena
428(2)
11.6.3 Challenges of Modeling Sustainable Petroleum Operations
430(3)
11.6.4 The Criterion: The Switch that Determines the Direction at a Bifurcation Point
433(19)
11.6.4.1 Some Applications of the Criterion
436(6)
11.7 The Need for Multidimensional Study
442(3)
11.8 Assessing the Overall Performance of a Process
445(7)
11.9 Implications of Knowledge-Based Analysis
452(13)
11.9.1 A General Case
452(3)
11.9.2 Impact of Global Warming Analysis
455(3)
11.9.3 Examples of Knowledge-based Simulation
458(7)
12 Reservoir Simulation of Unconventional Reservoirs 465(36)
12.1 Introduction
465(1)
12.2 Material Balance Equations
466(10)
12.3 New Fluid Flow Equations
476(2)
12.4 Coupled Fluid Flow and Geo-mechanical Stress Model
478(2)
12.5 Fluid Flow Modeling under Thermal Stress
480(1)
12.6 Challenges of Modeling Unconventional Gas Reservoirs
481(8)
12.7 Comprehensive Modeling
489(12)
12.7.1 Governing Equations
489(1)
12.7.2 Darcy's Model
490(1)
12.7.3 Forchheimer's Model
491(3)
12.7.4 Modified Brinkman's Model
494(2)
12.7.5 The Comprehensive Model
496(5)
13 Final Conclusions 501(4)
References and Bibliography 505(40)
Appendix A 545(24)
Index 569
M. R. Islam is Professor of Petroleum Engineering at the Civil and Resource Engineering Department of Dalhousie University, Canada. He has over 700 publications to his credit, including 6 books. He is on the editorial boards of several scholarly journals, and, in addition to his teaching duties, he is also director of Emertec Research and Development Ltd. and has been on the boards of a number of companies in North America and overseas./p>

Dr. S. Hossein Mousavizadegan is currently on the faculty of marine technology at the Amirkabir University of Technology in Tehran as an assistant professor, specializing in mathematical and numerical modeling of fluid dynamics.

Dr. Shabbir Mustafiz is a research engineer with the Alberta Research Council in Edmonton, Canada. Shabbir has published over 25 journal papers and has a Ph.D. in Civil Engineering, on the topic of petroleum reservoir simulation, from Dalhousie University and he is the current SPE Scholarship Chair for the Edmonton Section.

Jamal H. Abou-Kassem is Professor of Petroleum Engineering at the UAE U. in the United Arab Emirates, where he has taught since 1993. Abou-Kassem is a coauthor of two textbooks on reservoir simulation and an author or coauthor of numerous technical articles in the areas of reservoir simulation and other petroleum and natural gas-related topics.