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E-raamat: Shale Analytics: Data-Driven Analytics in Unconventional Resources

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 09-Feb-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319487533
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Shale Analytics: Data-Driven Analytics in Unconventional ResourcesSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 09-Feb-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319487533
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This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics , it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations.

Data-Driven Formation Evaluation - Generation of Synthetic Geo-mechanical Well Logs in Shale.- Data-Driven Reservoir Characteristics - Impact of rock and completion parameters in.- Data-Driven Completion Analysis - Analysis, Design and Optimization of Hydraulic Fracturing in Shale.- Data-Driven Reservoir Modeling - Full Field Reservoir Modeling of Marcellus Shale.- Data-Driven Reservoir Modeling - Full Field Reservoir Modeling of Niobrara Formation, DJ Basin.- Data-Driven Reservoir Modeling - AI-Based Proxy of Numerical Reservoir Simulation of Shale.
1 Introduction
1(6)
1.1 The Shale Revolution
2(2)
1.2 Traditional Modeling
4(1)
1.3 A Paradigm Shift
4(3)
2 Modeling Production from Shale
7(22)
2.1 Reservoir Modeling of Shale
9(1)
2.2 System of Natural Fracture Networks
10(3)
2.3 System of Natural Fracture Networks in Shale
13(1)
2.4 A New Hypothesis on Natural Fractures in Shale
14(2)
2.5 Consequences of Shale SNFN
16(2)
2.6 "Hard Data" Versus "Soft Data"
18(1)
2.7 Current State of Reservoir Simulation and Modeling of Shale
19(3)
2.7.1 Decline Curve Analysis
20(1)
2.7.2 Rate Transient Analysis
21(1)
2.8 Explicit Hydraulic Fracture Modeling
22(2)
2.9 Stimulated Reservoir Volume
24(3)
2.10 Microseismic
27(2)
3 Shale Analytics
29(54)
3.1 Artificial Intelligence
33(1)
3.2 Data Mining
33(2)
3.2.1 Steps Involved in Data Mining
34(1)
3.3 Artificial Neural Networks
35(20)
3.3.1 Structure of a Neural Network
36(2)
3.3.2 Mechanics of Neural Networks Operation
38(3)
3.3.3 Practical Considerations During the Training of a Neural Network
41(14)
3.4 Fuzzy Logic
55(7)
3.4.1 Fuzzy Set Theory
57(2)
3.4.2 Approximate Reasoning
59(1)
3.4.3 Fuzzy Inference
60(2)
3.5 Evolutionary Optimization
62(4)
3.5.1 Genetic Algorithms
63(1)
3.5.2 Mechanism of a Genetic Algorithm
64(2)
3.6 Cluster Analysis
66(2)
3.7 Fuzzy Cluster Analysis
68(2)
3.8 Supervised Fuzzy Cluster Analysis
70(13)
3.8.1 Well Quality Analysis (WQA)
71(3)
3.8.2 Fuzzy Pattern Recognition
74(9)
4 Practical Considerations
83(8)
4.1 Role of Physics and Geology
84(1)
4.2 Correlation is not the Same as Causation
84(2)
4.3 Quality Control and Quality Assurance of the Data
86(5)
5 Which Parameters Control Production from Shale
91(18)
5.1 Conventional Wisdom
92(1)
5.2 Shale Formation Quality
93(5)
5.3 Granularity
98(1)
5.4 Impact of Completion and Formation Parameters
98(8)
5.4.1 Results of Pattern Recognition Analysis
99(3)
5.4.2 Influence of Completion Parameters
102(4)
5.4.3 Important Notes on the Results and Discussion
106(1)
5.5
Chapter Conclusion and Closing Remarks
106(3)
6 Synthetic Geomechanical Logs
109(18)
6.1 Geomechanical Properties of Rocks
109(3)
6.1.1 Minimum Horizontal Stress
110(1)
6.1.2 Shear Modulus
110(1)
6.1.3 Bulk Modulus
110(1)
6.1.4 Young's Modulus
111(1)
6.1.5 Poisson's Ratio
112(1)
6.2 Geomechanical Well Logs
112(1)
6.3 Synthetic Model Development
113(11)
6.3.1 Synthetic Log Development Strategy
115(1)
6.3.2 Results of the Synthetic Logs
116(8)
6.4 Post-Modeling Analysis
124(3)
7 Extending the Utility of Decline Curve Analysis
127(26)
7.1 Decline Curve Analysis and Its Use in Shale
127(7)
7.1.1 Power Law Exponential Decline
129(1)
7.1.2 Stretched Exponential Decline
130(1)
7.1.3 Doung's Decline
130(2)
7.1.4 Tail-End Exponential Decline (TED)
132(2)
7.2 Comparing Different DC A Techniques
134(6)
7.2.1 Is One DCA Technique Better Than the Other?
136(4)
7.3 Extending the Utility of Decline Curve Analysis in Shale
140(11)
7.3.1 Impact of Different Parameters on DCA Technique
140(2)
7.3.2 Conventional Statistical Analysis Versus Shale Analytics
142(2)
7.3.3 More Results of Shale Analytics
144(7)
7.4 Shale Analytics and Decline Curve Analysis
151(2)
8 Shale Production Optimization Technology (SPOT)
153(76)
8.1 Dataset
153(2)
8.1.1 Production Data
153(1)
8.1.2 Hydraulic Fracturing Data
154(1)
8.1.3 Reservoir Characteristics Data
154(1)
8.2 Complexity of Well/Frac Behavior
155(9)
8.3 Well Quality Analysis (WQA)
164(11)
8.4 Fuzzy Pattern Recognition
175(8)
8.5 Key Performance Indicators (KPIs)
183(14)
8.6 Predictive Modeling
197(4)
8.6.1 Training, Calibration, and Validation of the Model
197(4)
8.7 Sensitivity Analysis
201(10)
8.7.1 Single-Parameter Sensitivity Analysis
202(6)
8.7.2 Combinatorial Sensitivity Analysis
208(3)
8.8 Generating Type Curves
211(9)
8.9 Look-Back Analysis
220(4)
8.10 Evaluating Service Companies' Performance
224(5)
9 Shale Numerical Simulation and Smart Proxy
229(22)
9.1 Numerical Simulation of Production from Shale Wells
229(4)
9.1.1 Discrete Natural Fracture Modeling
230(1)
9.1.2 Modeling the Induced Fractures
231(2)
9.2 Case Study: Marcellus Shale
233(4)
9.2.1 Geological (Static) Model
233(1)
9.2.2 Dynamic Model
234(1)
9.2.3 History Matching
235(2)
9.3 Smart Proxy Modeling
237(14)
9.3.1 A Short Introduction to Smart Proxy
237(1)
9.3.2 Cluster Level Proxy Modeling
238(2)
9.3.3 Model Development (Training and Calibration)
240(7)
9.3.4 Model Validation (Blind Runs)
247(4)
10 Shale Full Field Reservoir Modeling
251(16)
10.1 Introduction to Data-Driven Reservoir Modeling (Top-Down Modeling)
253(2)
10.2 Data from Marcellus Shale
255(4)
10.2.1 Well Construction Data
255(1)
10.2.2 Reservoir Characteristics Data
256(2)
10.2.3 Completion and Stimulation Data
258(1)
10.2.4 Production Data
258(1)
10.3 Pre-modeling Data Mining
259(1)
10.4 TDM Model Development
260(7)
10.4.1 Training and Calibration (History Matching)
260(2)
10.4.2 Model Validation
262(5)
11 Re-stimulation (Re-frac) of Shale Wells
267(12)
11.1 Re-frac Candidate Selection
268(4)
11.2 Re-frac Design
272(7)
Bibliography 279
Shahab D. Mohaghegh is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. A pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, he holds B.S., MS, and PhD degrees in petroleum and natural gas engineering. He has authored more than 180 technical papers and carried out more than 50 projects with major international companies. He is a SPE Distinguished Lecturer and has been featured in the Distinguished Author Series of SPEs Journal of Petroleum Technology (JPT) four times. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and has served as a member of U.S. Secretary of Energys Technical Advisory Committee on Unconventional Resources.
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