3.4 Fuzzy Logic Fuzzy Set Theory Approximate Reasoning Fuzzy Inference Evolutionary Optimization...

Transcription

1 Contents 1 Introduction The Shale Revolution Traditional Modeling A Paradigm Shift Modeling Production from Shale Reservoir Modeling of Shale System of Natural Fracture Networks System of Natural Fracture Networks in Shale A New Hypothesis on Natural Fractures in Shale Consequences of Shale SNFN Hard Data Versus Soft Data Current State of Reservoir Simulation and Modeling of Shale Decline Curve Analysis Rate Transient Analysis Explicit Hydraulic Fracture Modeling Stimulated Reservoir Volume Microseismic Shale Analytics Artificial Intelligence Data Mining Steps Involved in Data Mining Artificial Neural Networks Structure of a Neural Network Mechanics of Neural Networks Operation Practical Considerations During the Training of a Neural Network xi

2 xii Contents 3.4 Fuzzy Logic Fuzzy Set Theory Approximate Reasoning Fuzzy Inference Evolutionary Optimization Genetic Algorithms Mechanism of a Genetic Algorithm Cluster Analysis Fuzzy Cluster Analysis Supervised Fuzzy Cluster Analysis Well Quality Analysis (WQA) Fuzzy Pattern Recognition Practical Considerations Role of Physics and Geology Correlation is not the Same as Causation Quality Control and Quality Assurance of the Data Which Parameters Control Production from Shale Conventional Wisdom Shale Formation Quality Granularity Impact of Completion and Formation Parameters Results of Pattern Recognition Analysis Influence of Completion Parameters Important Notes on the Results and Discussion Chapter Conclusion and Closing Remarks Synthetic Geomechanical Logs Geomechanical Properties of Rocks Minimum Horizontal Stress Shear Modulus Bulk Modulus Young s Modulus Poisson s Ratio Geomechanical Well Logs Synthetic Model Development Synthetic Log Development Strategy Results of the Synthetic Logs Post-Modeling Analysis Extending the Utility of Decline Curve Analysis Decline Curve Analysis and Its Use in Shale Power Law Exponential Decline Stretched Exponential Decline

3 Contents xiii Doung s Decline Tail-End Exponential Decline (TED) Comparing Different DCA Techniques Is One DCA Technique Better Than the Other? Extending the Utility of Decline Curve Analysis in Shale Impact of Different Parameters on DCA Technique Conventional Statistical Analysis Versus Shale Analytics More Results of Shale Analytics Shale Analytics and Decline Curve Analysis Shale Production Optimization Technology (SPOT) Dataset Production Data Hydraulic Fracturing Data Reservoir Characteristics Data Complexity of Well/Frac Behavior Well Quality Analysis (WQA) Fuzzy Pattern Recognition Key Performance Indicators (KPIs) Predictive Modeling Training, Calibration, and Validation of the Model Sensitivity Analysis Single-Parameter Sensitivity Analysis Combinatorial Sensitivity Analysis Generating Type Curves Look-Back Analysis Evaluating Service Companies Performance Shale Numerical Simulation and Smart Proxy Numerical Simulation of Production from Shale Wells Discrete Natural Fracture Modeling Modeling the Induced Fractures Case Study: Marcellus Shale Geological (Static) Model Dynamic Model History Matching Smart Proxy Modeling A Short Introduction to Smart Proxy Cluster Level Proxy Modeling Model Development (Training and Calibration) Model Validation (Blind Runs) Shale Full Field Reservoir Modeling Introduction to Data-Driven Reservoir Modeling (Top-Down Modeling)

4 xiv Contents 10.2 Data from Marcellus Shale Well Construction Data Reservoir Characteristics Data Completion and Stimulation Data Production Data Pre-modeling Data Mining TDM Model Development Training and Calibration (History Matching) Model Validation Restimulation (Re-frac) of Shale Wells Re-frac Candidate Selection Re-frac Design Bibliography

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