Enterprise AI Analysis
Evaluation of lateral sealing capability of fault-controlled gas storage at different periods
This research provides a robust theoretical foundation for assessing sealing capabilities in gas storage facilities during subsequent development phases by systematically classifying fault types, characterizing sealing mechanisms, and quantifying dynamic sealing capacity.
Executive Impact
Discover the immediate and long-term implications of these findings for your enterprise, driving operational efficiency and strategic advantage.
Prediction Accuracy
Outperformance vs. Conventional
Annual Pressure Attenuation Rate
Hydrocarbon Saturation Threshold
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 Prediction Accuracy
89% Model prediction accuracy, outperforming conventional methods by 32%.Our machine learning-assisted history matching across 12 injection-production cycles demonstrates a significant leap in accuracy for predicting fault sealing behavior. This high fidelity ensures reliable operational planning and risk mitigation for gas storage facilities.
SGR Threshold for Sealing Efficiency
35% Critical Shale Gouge Ratio (SGR) for exponential increase in sealing efficiency.Fault zone clay content (SGR) is the predominant control on fault sealing capacity. Below 35% SGR, sealing efficiency is considerably lower, highlighting a crucial threshold for risk assessment and site selection.
Pressure Differential Decay Rate
4.7% Annual attenuation rate of pressure differentials across faults.Dynamic simulations reveal that pressure differentials across faults exhibit a temporal decay, governed primarily by fault zone permeability and hydrocarbon migration distance. Understanding this decay rate is vital for long-term storage integrity.
Enterprise Process Flow: Fault Sealing Assessment
Statistical Analysis of Fault Parameters (Dip, Thickness)
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Integrated Fault Sealing Triangle Diagram from Logging Data
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Anatomical Examination of Original Gas Reservoir for Sealing Capacity
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Analysis of Dynamic Development Data for Pressure Variations
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Numerical Simulation with Time Effects for Dynamic Sealing Assessment
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Model Establishment & History Matching (Machine Learning)
This systematic methodology combines geological characterization with dynamic operational data and advanced simulation techniques, including machine learning, to provide a comprehensive assessment of fault sealing capabilities in gas storage reservoirs.
Data Assimilation Techniques
4D Seismic & PLT Integration of multi-scale data for enhanced characterization.Our advanced methodology integrates multi-scale data, including 4D seismic monitoring and production logging tools (PLT) data, to provide a holistic view of reservoir dynamics and fault integrity.
Comparison of Fault Sealing Mechanisms
| Feature | Lithological Contact Sealing | Fault Rock Sealing |
|---|---|---|
| Underlying Mechanism |
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| Key Controlling Factor |
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| Typical Scenario |
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| Performance in L Gas Storage |
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Understanding the distinct mechanisms of fault sealing is critical for accurate risk assessment. While lithological contact sealing often provides robust containment, fault rock sealing requires careful evaluation due to its variable effectiveness based on clay content and dynamic conditions.
Case Study: F2 Fault Sealing Evolution
Challenge: The F2 fault showed some geological sealing ability but demonstrated significantly enhanced capacity during development, exceeding geological predictions.
Solution: Dynamic development data analysis coupled with numerical simulation revealed that during active injection/production cycles, the F2 fault withstood pressure differentials (2.5-2.78 MPa) far greater than its geological closure capacity (0.263-0.312 MPa).
Impact: This highlights the inadequacy of traditional static geological models for dynamic operations and validates the need for time-dependent sealing capacity assessments. It suggests that faults can exhibit much higher dynamic sealing capacities than their static counterparts.
This case illustrates that fault sealing capacity is not static but evolves with operational dynamics, requiring advanced models for accurate real-time risk management.
Maximum Differential Pressure F2 Fault (Dynamic)
2.5-2.78 MPa Pressure differential sustained by F2 fault during development.The F2 fault, despite initial geological assessments of weaker sealing, demonstrates a robust dynamic sealing capacity, capable of withstanding significant pressure differentials during active gas storage operations.
Maximum Differential Pressure F4 Fault (Geological)
0.074-0.089 MPa Original geological closure capacity of F4 fault.Geologically, the F4 fault had a low closure capacity. During development, it exhibited leakage, emphasizing the disparity between static and dynamic sealing potential.
Case Study: F4 Fault Leakage During Development
Challenge: The F4 fault was considered sealed geologically, but actual injection and production data indicated leakage during development, failing to maintain the seal.
Solution: Our integrated dynamic evaluation model revealed that despite leakage, the F4 fault still offers some resistance to fluid flow and can sustain pressure differences exceeding its historical closure capacity over short time frames.
Impact: This finding is crucial for understanding that 'leakage' isn't always a complete failure but can represent a dynamic equilibrium where fluid migration is slowed, allowing for targeted mitigation strategies. It underscores the importance of real-time monitoring and dynamic modeling.
This case demonstrates that static geological assessments are insufficient for predicting fault behavior under dynamic operational conditions, necessitating continuous monitoring and adaptive management strategies.
The study concludes that fault zone clay content (SGR) is the primary determinant of sealing capacity, with a critical threshold at 35% SGR for exponential sealing efficiency. Our 3D geomechanical model, integrated with paleo-gas reservoir anatomy, offers superior accuracy in predicting lateral sealing. Dynamic simulations highlight a temporal decay of pressure differentials across faults, with an annual attenuation rate of 4.7% driven by fault zone permeability and hydrocarbon migration distance. The enhanced characterization methodology combines 4D seismic monitoring, production logging tools, time-dependent permeability decay functions, and machine learning-assisted history matching, achieving 89% prediction accuracy—32% higher than traditional static approaches. This reveals two evolutionary phases: short-term pressure differentials dominated by petrophysical heterogeneity, and long-term equilibration controlled by SGR evolution (where SGR ≥ 38% enhances sealing efficiency 2.3x). A critical transition occurs when hydrocarbon saturation differentials drop below 35%, reducing cross-fault flow potential by 72%.
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