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Reservoir Characterization: Fundamentals and Applications, Volume 2 [Kõva köide]

(University of Southern California, CA; University of Houston, TX)
  • Formaat: Hardback, 576 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 25-Jan-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 111955621X
  • ISBN-13: 9781119556213
Teised raamatud teemal:
Reservoir Characterization: Fundamentals and Applications, Volume 2Suurem pilt
  • Formaat: Hardback, 576 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 25-Jan-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 111955621X
  • ISBN-13: 9781119556213
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119556213.html
Märksõnad:
RESERVOIR CHARACTERIZATION FUNDAMENTALS AND APPLICATIONS

The 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.

Long 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.

This 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 articles in this volume have been updated, and there are some new additions, as well, to keep the engineer abreast of any updates and new methods in the industry.

Truly a snapshot of the state of the art, this groundbreaking volume is a must-have for any petroleum engineer working in the field, environmental engineers, petroleum engineering students, and any other engineer or scientist working with reservoirs.

This outstanding new volume:

  • Is a collection of papers on reservoir characterization written by world-renowned engineers and scientists and presents them here, in one volume
  • Contains in-depth coverage of not just the fundamentals of reservoir characterization, but the anomalies and challenges, set in application-based, real-world situations
  • Covers reservoir characterization for the engineer to be able to solve daily problems on the job, whether in the field or in the office
  • Deconstructs myths that are prevalent and deeply rooted in the industry and reconstructs logical solutions
  • Is a valuable resource for the veteran engineer, new hire, or petroleum engineering student
Foreword xix
Preface xxiii
Part 1: Introduction 1(22)
1 Reservoir Characterization: Fundamental and Applications - An Overview
3(20)
Fred Aminzadeh
1.1 Introduction to Reservoir Characterization?
3(2)
1.2 Data Requirements for Reservoir Characterization
5(2)
1.3 SURE Challenge
7(3)
1.4 Reservoir Characterization in the Exploration, Development and Production Phases
10(2)
1.4.1 Exploration Stage/Development Stage
10(1)
1.4.2 Primary Production Stage
11(1)
1.4.3 Secondary/Tertiary Production Stage
11(1)
1.5 Dynamic Reservoir Characterization (DRC)
12(3)
1.5.1 4D Seismic for DRC
13(1)
1.5.2 Microseismic Data for DRC
14(1)
1.6 More on Reservoir Characterization and Reservoir Modeling for Reservoir Simulation
15(5)
1.6.1 Rock Physics
16(1)
1.6.2 Reservoir Modeling
17(3)
1.7 Conclusion
20(1)
References
20(3)
Part 2: General Reservoir Characterization and Anomaly Detection 23(172)
2 A Comparison Between Estimated Shear Wave Velocity and Elastic Modulus by Empirical Equations and that of Laboratory Measurements at Reservoir Pressure Condition
25(22)
Haleh Azizia
Hamid Reza Siahkoohi
Brian Evans
Nasser Keshavarz Farajkhah
Ezatollah KazemZadeh
2.1 Introduction
26(2)
2.2 Methodology
28(4)
2.1.2 Estimating the Shear Wave Velocity
28(3)
2.2.2 Estimating Geomechanical Parameters
31(1)
2.3 Laboratory Set Up and Measurements
32(3)
2.3.1 Laboratory Data Collection
34(1)
2.4 Results and Discussion
35(6)
2.5 Conclusions
41(2)
2.6 Acknowledgment
43(1)
References
43(4)
3 Anomaly Detection within Homogenous Geologic Area
47(22)
Simon Katz
Fred Aminzadeh
George Chilingar
Leonid Khilyuk
3.1 Introduction
48(1)
3.2 Anomaly Detection Methodology
49(1)
3.3 Basic Anomaly Detection Classifiers
50(2)
3.4 Prior and Posterior Characteristics of Anomaly Detection Performance
52(3)
3.5 ROC Curve Analysis
55(3)
3.6 Optimization of Aggregated AD Classifier Using Part of the Anomaly Identified by Universal Classifiers
58(3)
3.7 Bootstrap Based Tests of Anomaly Type Hypothesis
61(3)
3.8 Conclusion
64(1)
References
65(4)
4 Characterization of Carbonate Source-Derived Hydrocarbons Using Advanced Geochemical Technologies
69(12)
Hossein Alimi
4.1 Introduction
70(1)
4.2 Samples and Analyses Performed
71(1)
4.3 Results and Discussions
72(7)
4.4 Summary and Conclusions
79(1)
References
80(1)
5 Strategies in High-Data-Rate MWD Mud Pulse Telemetry
81(54)
Yinao Su
Limin Sheng
Lin Li
Hailong Bian
Rong Shi
Xiaoying Zhuang
Wilson Chin
5.1 Summary
82(6)
5.1.1 High Data Rates and Energy Sustainability
82(1)
5.1.2 Introduction
83(2)
5.1.3 MWD Telemetry Basics
85(2)
5.1.4 New Telemetry Approach
87(1)
5.2 New Technology Elements
88(23)
5.2.1 Downhole Source and Signal Optimization
89(3)
5.2.2 Surface Signal Processing and Noise Removal
92(1)
5.2.3 Pressure, Torque and Erosion Computer Modeling
93(3)
5.2.4 Wind Tunnel Analysis: Studying New Approaches
96(12)
5.2.5 Example Test Results
108(3)
5.3 Directional Wave Filtering
111(21)
5.3.1 Background Remarks
111(1)
5.3.2 Theory
112(4)
5.3.3 Calculations
116(16)
5.4 Conclusions
132(1)
Acknowledgments
133(1)
References
133(2)
6 Detection of Geologic Anomalies with Monte Carlo Clustering Assemblies
135(16)
Simon Katz
Fred Aminzadeh
George Chilingar
Leonid Khilyuk
Matin Lockpour
6.1 Introduction
135(1)
6.2 Analysis of Inhomogeneity of the Training and Test Sets and Instability of Clustering
136(2)
6.3 Formation of Multiple Randomized Test Sets and Construction of the Clustering Assemblies
138(1)
6.4 Irregularity Index of Individual Clusters in the Cluster Set
139(2)
6.5 Anomaly Indexes of Individual Records and Clustering Assemblies
141(1)
6.6 Prior and Posterior True and False Discovery Rates for Anomalous and Regular Records
142(1)
6.7 Estimates of Prior False Discovery Rates for Anomalous Cluster Sets, Clusters, and Individual Records. Permeability Dataset
142(2)
6.8 Posterior Analysis of Efficiency of Anomaly Identification. High Permeability Anomaly
144(2)
6.9 Identification of Records in the Gas Sand Dataset as Anomalous, using Brine Sand Dataset as Data with Regular Records
146(3)
6.10 Notations
149(1)
6.11 Conclusions
149(1)
References
150(1)
7 Dissimilarity Analysis of Petrophysical Parameters as Gas-Sand Predictors
151(18)
Simon Katz
George Chilingar
Fred Aminzadeh
Leonid Khilyuk
7.1 Introduction
152(1)
7.2 Petrophysical Parameters for Gas-Sand Identification
152(2)
7.3 Lithologic and Fluid Content Dissimilarities of Values of Petrophysical Parameters
154(1)
7.4 Parameter Ranking and Efficiency of Identification of Gas-Sands
155(4)
7.5 ROC Curve Analysis with Cross Validation
159(2)
7.6 Ranking Parameters According to AUC Values
161(2)
7.7 Classification with Multidimensional Parameters as Gas Predictors
163(1)
7.8 Conclusions
164(2)
Definitions and Notations
166(1)
References
166(3)
8 Use of Type Curve for Analyzing Non-Newtonian Fluid Flow Tests Distorted by Wellbore Storage Effects
169(26)
Fand Siddiqui
Mohamed Y. Soliman
8.1 Introduction
170(3)
8.2 Objective
173(1)
8.3 Problem Analysis
173(3)
8.3.1 Model Assumptions
174(1)
8.3.2 Solution Without the Wellbore Storage Distortion
175(1)
8.3.3 Wellbore Storage and Skin Effects
175(1)
8.3.4 Solution by Mathematical Inspection
175(1)
8.3.5 Solution Verification
176(1)
8.4 Use of Finite Element
176(1)
8.5 Analysis Methodology
177(3)
8.5.1 Finding the n Value
177(1)
8.5.2 Dimensionless Wellbore Storage
178(1)
8.5.3 Use of Type Curves
178(1)
8.5.4 Match Point
179(1)
8.5.5 Uncertainty in Analysis
180(1)
8.6 Test Data Examples
180(8)
8.6.1 Match Point
182(1)
8.6.2 Match Point
183(2)
8.6.3 Analysis Recommendations
185(1)
8.6.4 Match Point
185(1)
8.6.5 Analysis Recommendations
186(1)
8.6.6 Match point
186(2)
8.7 Conclusion
188(1)
Nomenclature
188(1)
References
189(1)
Appendix A: Non-Linear Boundary Condition and Laplace Transform
189(2)
Appendix B: Type Curve Charts for Various Power Law Indices
191(4)
Part 3: Reservoir Permeability Detection 195(58)
9 Permeability Prediction Using Machine Learning, Exponential, Multiplicative, and Hybrid Models
197(20)
Simon Katz
Fred Aminzadeh
George Chilingar
M. Lackpour
9.1 Introduction
197(1)
9.2 Additive, Multiplicative, Exponential, and Hybrid Permeability Models
198(2)
9.3 Combination of Basis Function Expansion and Exhaustive Search for Optimum Subset of Predictors
200(1)
9.4 Outliers in the Forecasts Produced with Four Permeability Models
201(2)
9.5 Additive, Multiplicative, and Exponential Committee Machines
203(3)
9.6 Permeability Forecast with First Level Committee Machines. Sandstone Dataset
206(4)
9.7 Permeability Prediction with First Level Committee Machines. Carbonate Reservoirs
210(2)
9.8 Analysis of Accuracy of Outlier Replacement by The First and Second Level Committee Machines. Sandstone Dataset
212(2)
9.9 Conclusion
214(1)
Notations and Definitions
215(1)
References
216(1)
10 Geological and Geophysical Criteria for Identifying Zones of High Gas Permeability of Coals (Using the Example of Kuzbass CBM Deposits)
217(14)
A.G. Pogosyan
10.1 Introduction
217(2)
10.2 Physical Properties and External Load Conditions on a Coal Reservoir
219(6)
10.3 Basis for Evaluating Physical and Mechanical Coalbed Properties in the Borehole Environment
225(3)
10.4 Conclusions
228(1)
Acknowledgement
228(1)
References
229(2)
11 Rock Permeability Forecasts Using Machine Learning and Monte Carlo Committee Machines
231(22)
Simon Katz
Fred Aminzadeh
Wennan Long
George Chilingar
Matin Lackpour
11.1 Introduction
232(1)
11.2 Monte Carlo Cross Validation and Monte Carlo Committee Machines
233(3)
11.3 Performance of Extended MC Cross Validation and Construction MC Committee Machines
236(1)
11.4 Parameters of Distribution of the Number of Individual Forecasts in Monte Carlo Cross Validation
237(1)
11.5 Linear Regression Permeability Forecast with Empirical Permeability Models
238(4)
11.6 Accuracy of the Forecasts with Machine Learning Methods
242(2)
11.7 Analysis of Instability of the Forecast
244(2)
11.8 Enhancement of Stability of the MC Committee Machines Forecast Via Increase of the Number of Individual Forecasts
246(1)
11.9 Conclusions
247(1)
Nomenclature
247(1)
Appendix 1 Description of Permeability Models from Different Fields
248(1)
Appendix 2 A Brief Overview of Modular Networks or Committee Machines
249(2)
References
251(2)
Part 4: Reserves Evaluation/Decision Making 253(84)
12 The Gulf of Mexico Petroleum System - Foundation for Science-Based Decision Making
255(14)
Corinne Disenhof
MacKenzie Mark-Moser
Kelly Rose
Introduction
256(1)
Basin Development and Geologic Overview
257(2)
Petroleum System
259(1)
Reservoir Geology
259(2)
Hydrocarbons
261(1)
Salt and Structure
262(1)
Conclusions
263(1)
Acknowledgments and Disclaimer
264(1)
References
265(4)
13 Forecast and Uncertainty Analysis of Production Decline Trends with Bootstrap and Monte Carlo Modeling
269(20)
Simon Katz
George Chilingar
Leonid Khilyuk
13.1 Introduction
270(1)
13.2 Simulated Decline Curves
271(2)
13.3 Nonlinear Least Squares for Decline Curve Approximation
273(1)
13.4 New Method of Grid Search for Approximation and Forecast of Decline Curves
273(2)
13.5 Iterative Minimization of Least Squares with Multiple Approximating Models
275(1)
13.6 Grid Search Followed by Iterative Minimization with Levenberg-Marquardt Algorithm
276(1)
13.7 Two Methods for Aggregated Forecast and Analysis of Forecast Uncertainty
277(2)
13.8 Uncertainty Quantile Ranges Obtained Using Monte Carlo and Bootstrap Methods
279(1)
13.9 Monte Carlo Forecast and Analysis of Forecast Uncertainty
280(4)
13.10 Block Bootstrap Forecast and Analysis of Forecast Uncertainty
284(1)
13.11 Comparative Analysis of Results of Monte Carlo and Bootstrap Simulations
285(2)
13.12 Conclusions
287(1)
References
288(1)
14 Oil and Gas Company Production, Reserves, and Valuation
289(48)
Mark J. Kaiser
14.1 Introduction
290(2)
14.2 Reserves
292(2)
14.2.1 Proved Reserves
292(1)
14.2.2 Proved Reserves Categories
292(1)
14.2.3 Reserves Reporting
293(1)
14.2.4 Probable and Possible Reserves
293(1)
14.2.5 Contractual Differences
294(1)
14.3 Production
294(1)
14.4 Factors that Impact Company Value
295(8)
14.4.1 Ownership
295(1)
14.4.1.1 International Oil Companies
295(1)
14.4.1.2 National Oil Companies
296(1)
14.4.1.3 Government Sponsored Entities
296(1)
14.4.1.4 Independents and Juniors
297(1)
14.4.2 Degree of Integration
297(1)
14.4.3 Product Mix
298(1)
14.4.4 Commodity Price
298(1)
14.4.5 Production Cost
299(1)
14.4.6 Finding Cost
299(1)
14.4.7 Assets
300(1)
14.4.8 Capital Structure
300(1)
14.4.9 Geologic Diversification
301(1)
14.4.10 Geographic Diversification
301(1)
14.4.11 Unobservable Factors
302(1)
14.5 Summary Statistics
303(6)
14.5.1 Sample
303(1)
14.5.2 Variables
303(2)
14.5.3 Data Source
305(1)
14.5.4 International Oil Companies
305(3)
14.5.5 Independents
308(1)
14.6 Market Capitalization
309(1)
14.6.1 Functional Specification
309(1)
14.6.2 Expectations
309(1)
14.7 International Oil Companies
310(2)
14.8 U.S. Independents
312(6)
14.8.1 Large vs. Small Cap, Oil vs. Gas
312(2)
14.8.2 Consolidated Small-Caps
314(1)
14.8.3 Multinational vs. Domestic
314(1)
14.8.4 Conventional vs. Unconventional
315(1)
14.8.5 Production and Reserves
316(1)
14.8.6 Regression Models
316(2)
14.9 Private Companies
318(2)
14.10 National Oil Companies of OPEC
320(1)
14.11 Government Sponsored Enterprises and Other International Companies
320(3)
14.12 Conclusions
323(1)
References
324(13)
Part 5: Unconventional Reservoirs 337(90)
15 An Analytical Thermal-Model for Optimization of Gas-Drilling in Unconventional Tight-Sand Reservoirs
339(24)
Boyun Guo
Gao Li
Jinze Song
15.1 Introduction
340(1)
15.2 Mathematical Model
341(5)
15.3 Model Comparison
346(2)
15.4 Sensitivity Analysis
348(1)
15.5 Model Applications
349(2)
15.6 Conclusions
351(1)
Nomenclature
352(1)
Acknowledgements
353(1)
References
353(2)
Appendix A: Steady Heat Transfer Solution for Fluid Temperature in Counter-Current Flow
355(1)
Assumptions
355(1)
Governing Equation
355(5)
Boundary Conditions
360(1)
Solution
360(3)
16 Development of an Analytical Model for Predicting the Fluid Temperature Profile in Drilling Gas Hydrates Reservoirs
363(20)
Liqun Shan
Boyun Guo
Xiao Cai
16.1 Introduction
364(1)
16.2 Mathematical Model
365(8)
16.3 Case Study
373(1)
16.4 Sensitivity Analysis
374(3)
16.5 Conclusions
377(1)
Acknowledgements
378(1)
Nomenclature
378(1)
References
379(4)
17 Distinguishing Between Brine-Saturated and Gas-Saturated Shaly Formations with a Monte-Carlo Simulation of Seismic Velocities
383(16)
Simon Katz
George Chilingar
Leonid Khilyuk
17.1 Introduction
384(1)
17.2 Random Models for Seismic Velocities
385(2)
17.3 Variability of Seismic Velocities Predicted by Random Models
387(1)
17.4 The Separability of (Vp, Vs) Clusters for Gas- and Brine-Saturated Formations
388(1)
17.5 Reliability Analysis of Identifying Gas-Filled Formations
389(7)
17.5.1 Classification with K-Nearest Neighbor
391(1)
17.5.2 Classification with Recursive Partitioning
392(2)
17.5.3 Classification with Linear Discriminant Analysis
394(1)
17.5.4 Comparison of the Three Classification Techniques
395(1)
17.6 Conclusions
396(1)
References
397(2)
18 Shale Mechanical Properties Influence Factors Overview and Experimental Investigation on Water Content Effects
399(28)
Hui Li
Bitao Lai
Shuhua Lin
18.1 Introduction
400(1)
18.2 Influence Factors
400(14)
18.2.1 Effective Pressure
401(1)
18.2.2 Porosity
402(1)
18.2.3 Water Content
403(2)
18.2.4 Salt Solutions
405(1)
18.2.5 Total Organic Carbon (TOC)
406(1)
18.2.6 Clay Content
407(1)
18.2.7 Bedding Plane Orientation
408(3)
18.2.8 Mineralogy
411(2)
18.2.9 Anisotropy
413(1)
18.2.10 Temperature
413(1)
18.3 Experimental Investigation of Water Saturation Effects on Shale's Mechanical Properties
414(4)
18.3.1 Experiment Description
414(1)
18.3.2 Results and Discussion
414(3)
18.3.3 Error Analysis of Experiments
417(1)
18.4 Conclusions
418(2)
Acknowledgements
420(1)
References
420(7)
Part 6: Enhance Oil Recovery 427(60)
19 A Numerical Investigation of Enhanced Oil Recovery Using Hydrophilic Nanofuids
429(34)
Yin Feng
Liyuan Cao
Erxiu Shi
19.1 Introduction
430(2)
19.2 Simulation Framework
432(5)
19.2.1 Background
432(1)
19.2.2 Two Essential Computational Components
433(1)
19.2.2.1 Flow Model
433(1)
19.2.2.2 Nanoparticle Transport and Retention Model
435(2)
19.3 Coupling of Mathematical Models
437(2)
19.4 Verification Cases
439(4)
19.4.1 Effect of Time Steps on the Performance of the in House Simulator
439(1)
19.4.2 Comparison with Eclipse
440(2)
19.4.3 Comparison with Software MNM1D
442(1)
19.5 Results
443(14)
19.5.1 Continuous Injection
445(1)
19.5.1.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption
445(1)
19.5.1.2 Effect of Injection Rate on Oil Recovery and Nanoparticle Adsorption
447(2)
19.5.2 Slug Injection
449(1)
19.5.2.1 Effect of Injection Time on Oil Recovery and Nanoparticle Adsorption
449(1)
19.5.2.2 Effect of Slug Size on Oil Recovery and Nanoparticle Adsorption
451(1)
19.5.3 Water Postflush
452(1)
19.5.3.1 Effect of Injection Time Length
452(1)
19.5.3.2 Effect of Flow Rate Ratio Between Water and Nanofluids on Oil and Nanoparticle Recovery
452(3)
19.5.4 3D Model Showcase
455(2)
19.6 Discussions
457(2)
19.7 Conclusions and Future Work
459(2)
References
461(2)
20 3D Seismic-Assisted CO2-EOR Flow Simulation for the Tensleep Formation at Teapot Dome, USA
463(24)
Payam Kavousi Ghahfarokhi
Thomas H. Wilson
Alan Lee Brown
20.1 Presentation Sequence
464(1)
20.2 Introduction
464(4)
20.3 Geological Background
468(1)
20.4 Discrete Fracture Network (DFN)
469(4)
20.5 Petrophysical Modeling
473(1)
20.6 PVT Analysis
473(6)
20.7 Streamline Analysis
479(1)
20.8 CO2-EOR
479(4)
20.9 Conclusions
483(1)
Acknowledgement
483(1)
References
484(3)
Part 7: New Advances in Reservoir Characterization-Machine Learning Applications 487(38)
21 Application of Machine Learning in Reservoir Characterization
489(36)
Fred Aminzadeh
21.1 Brief Introduction to Reservoir Characterization
489(2)
21.2 Artificial Intelligence and Machine (Deep) Learning Review
491(11)
21.2.1 Support Vector Machines
492(1)
21.2.2 Clustering (Unsupervised Classification)
492(5)
21.2.3 Ensemble Methods
497(1)
21.2.4 Artificial Neural Networks (ANN)-Based Methods
498(4)
21.3 Artificial Intelligence and Machine (Deep) Learning Applications to Reservoir Characterization
502(11)
21.3.1 3D Structural Model Development
503(3)
21.3.2 Sedimentary Modeling
506(2)
21.3.3 3D Petrophysical Modeling
508(4)
21.3.4 Dynamic Modeling and Simulations
512(1)
21.4 Machine (Deep) Learning and Enhanced Oil Recovery (EOR)
513(4)
21.4.1 ANNs for EOR Performance and Economics
514(2)
21.4.2 ANNs for EOR Screening
516(1)
21.5 Conclusion
517(1)
Acknowledgement
518(1)
References
518(7)
Index 525
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.