Geomechanical Flow Simulation for Leakage Risk Quantification
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Solution Overview
Problem
Current methods for assessing leakage risks in carbon capture and storage (CCS) projects are inadequate, as they fail to accurately quantify the probability and severity of CO2 leakage and are often computationally intensive.
Innovation Solution
The development of a method that involves performing multiple simulated injections using geomechanical and fluid flow simulations on a subsurface model of a geological storage complex, allowing for the calculation of leakage probabilities and severities, and ultimately determining a leakage risk based on these values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple simulated injections with varied model parameters are performed to improve leakage risk quantification accuracy, then measurement precision of leakage probability and severity is improved, but device complexity and computational resources increase
Solution Approach 1:
The simulation system is segmented into modular components: geomechanical simulator, fluid flow simulator, and risk assessment module. Each module handles specific aspects of the simulation independently, allowing for targeted computation and reducing overall system complexity while maintaining comprehensive risk evaluation through integration of results from multiple simulated injections with varied model parameters.
2Measurement precision
If high-fidelity simulations with fine grid resolutions are used to improve prediction accuracy, then measurement precision is improved, but productivity and computational efficiency deteriorate
Solution Approach 1:
The system performs multiple simulated injections with varied model parameters rather than a single high-resolution simulation. This approach distributes computational effort across multiple runs with different parameter sets, achieving comprehensive risk quantification through statistical aggregation of results while avoiding the prohibitive computational cost of a single ultra-fine grid simulation.
Solution Approach 2:
Model parameters are varied and prepared in advance for multiple simulated injections. This preliminary setup allows the system to efficiently execute a series of simulations with pre-configured parameter sets, reducing runtime computational overhead and improving overall productivity while maintaining prediction accuracy through ensemble-based risk assessment.
3Difficulty of detecting and measuring
If comprehensive monitoring and simulation methods are deployed to improve leakage detection capability, then measurement precision of leakage detection is improved, but device complexity and operational costs increase
Solution Approach 1:
The system uses numerical simulators as intermediaries to model and predict leakage behavior. Rather than deploying complex physical monitoring infrastructure throughout the storage complex, the simulators act as virtual sensors that compute leakage probabilities and severities based on geomechanical and fluid flow models, providing comprehensive detection capability through computational rather than purely physical means.
Data Source
AI summary
Certain aspects of the disclosure provide systems and methods for quantifying leakage risk in a geological storage complex. A method may include performing a plurality of simulated injections by executing geomechanical and fluid flow simulations on a subsurface model representing a geological storage complex, where model parameters are varied for one or more simulated injections. The method may include determining, for the one or more simulated injections, one or more leakage volumes for one or more surface locations in the geological storage complex, and calculating, for the one or more surface locations, one or more of: a leakage probability value indicating a simulated probability of leakage occurring at the surface location, or a leakage severity value indicating a simulated average amount of leakage volume at the surface location. The method may include determining leakage risk based on one or more of the leakage probability value or the leakage severity value.