Enterprise AI Analysis
Evaluation of lateral sealing capability of fault-controlled gas storage at different periods
This comprehensive AI-powered analysis provides a deep dive into the critical aspects of fault sealing in gas storage, leveraging advanced methodologies and dynamic data.
Executive Impact: Key Metrics for Enterprise AI
Our AI analysis of the article reveals critical performance indicators for optimizing fault sealing in gas storage.
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Prediction Accuracy for 12 Injection-Production Cycles
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Outperformance vs. Conventional Methods
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SGR Threshold for Exponential Sealing Efficiency Increase
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Annual Attenuation Rate of Pressure Differentials
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Fault sealing in gas storage relies on two primary mechanisms: lithological contact sealing and fault rock sealing. Lithological contact sealing occurs when permeable reservoir rocks are juxtaposed against impermeable cap rocks or shales across a fault. Fault rock sealing, on the other hand, involves the development of low-permeability fault gouge or clay smears within the fault zone itself, which acts as a barrier to fluid flow. The effectiveness of these mechanisms is crucial for preventing lateral gas leakage.
The Shale Gouge Ratio (SGR) is a key parameter for quantifying fault rock sealing capacity. It represents the proportion of clay-rich components within a fault zone. Higher SGR values generally correlate with greater sealing potential due to increased capillary forces. The study identifies a critical SGR threshold of 35%, above which sealing efficiency increases exponentially, reaching up to 89% prediction accuracy in dynamic simulations.
Traditional static methods for fault sealing assessment are often inadequate for dynamic gas storage operations. This analysis introduces a dynamic sealing capacity model that accounts for time-variant pressure differentials and fluid properties during injection and production cycles. It reveals that the sealing capacity of faults can vary significantly between geological history and operational phases, with some faults showing enhanced sealing under dynamic conditions, while others, previously sealed, may leak.
89%
Prediction Accuracy in Dynamic Sealing Model
Enhanced Fault Sealing Characterization Methodology
Multi-Scale Data Assimilation (4D Seismic, PLT)
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Time-Dependent Permeability Decay Functions (Core Flooding Calibrated)
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Machine Learning-Assisted History Matching (12 Cycles)
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Case Study: L Gas Storage Facility Fault Behavior
The L gas storage facility served as a critical case study. Analysis revealed that while F4 fault was geologically sealed, it exhibited leakage during development. Conversely, F2 fault demonstrated enhanced sealing capacity during dynamic operations, withstanding pressure differentials significantly exceeding its geological sealing capability. This highlights the inadequacy of traditional static methods for assessing operational safety and the necessity of dynamic modeling.
Specifically, F2 could withstand 2.5 MPa pressure differential dynamically, compared to 0.263 MPa geologically. F4, while sealed geologically at 0.074 MPa, failed to maintain seal during development.
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