Geological Site Framework Using Sensitivity Ranking for Reservoir Assessment
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Solution Overview
Problem
Assessing the suitability of subsurface geological sites for purposes such as CO2 storage is complex due to limited or incomparable data, requiring significant resources and lacking effective methods to rank factors influencing site selection.
Innovation Solution
Implementing a framework that utilizes Sobol' or Kucherenko indices techniques for global sensitivity analysis to generate confidence intervals and rank factors influencing reservoir properties, supported by a graphical user interface for site assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional site assessment methods are used with limited data, then resource consumption is reduced, but measurement precision and reliability of site suitability assessment deteriorate
Solution Approach 1:
The framework performs preliminary global sensitivity analysis before detailed site assessment to identify which parameters most influence assessment outcomes. This preliminary ranking allows subsequent assessments to focus resources on measuring and analyzing only the most critical parameters, thereby improving precision without proportionally increasing resource consumption across all parameters.
Solution Approach 2:
The system dynamically changes the set of parameters considered in site assessments based on sensitivity analysis results. By adjusting which parameters are fully analyzed versus which are held constant or given default values, the framework optimizes the balance between assessment precision and resource consumption for each specific assessment scenario.
2Measurement precision
If comprehensive data collection is performed for all reservoir sites, then measurement precision improves, but loss of time and productivity deteriorate
Solution Approach 1:
The framework extracts and identifies the most influential parameters through global sensitivity analysis, separating them from less important parameters. This extraction allows the assessment process to focus comprehensive data collection and analysis only on the critical few parameters that most affect site suitability, while using default or simplified approaches for the remaining parameters, thereby reducing total assessment time while maintaining accuracy.
Solution Approach 2:
Instead of performing exhaustive analysis on all parameters equally, the framework applies partial action by concentrating detailed analysis resources on the top-ranked sensitive parameters while applying simplified or default treatments to less sensitive parameters, achieving sufficient assessment accuracy with reduced time investment.
3Reliability
If multiple factors are considered in site ranking, then reliability of site selection improves, but device complexity and difficulty of detecting and measuring worsen
Solution Approach 1:
The framework segments the complex site assessment process into distinct modules: global sensitivity analysis to rank parameters, weighted scoring to evaluate sites, and comparative ranking to select optimal sites. This segmentation allows each module to handle specific aspects of the assessment independently, making the overall complex system more manageable and implementable while considering multiple factors for reliable site selection.
Solution Approach 2:
The system changes parameters dynamically based on sensitivity analysis results, adjusting which parameters are actively evaluated versus which use default values. This parameter adaptation simplifies the effective complexity of the assessment framework for each specific application while maintaining reliability by ensuring that the most influential parameters receive appropriate attention.
4Loss of information
If global sensitivity analysis is implemented to rank factors, then loss of information is reduced, but device complexity and computational requirements increase
Solution Approach 1:
The framework replaces complex, resource-intensive detailed assessments with a computational global sensitivity analysis that uses statistical methods to rank parameter importance. This substitution uses mathematical computations rather than exhaustive physical or experimental investigations, capturing essential information about influential factors while reducing overall computational and operational complexity.
Data Source
AI summary
A method can include accessing data for a number of reservoir sites, where the data include at least property data for reservoir properties; performing a determination, using the property data for the reservoir sites, as to whether the reservoir properties for the reservoir sites are independent; responsive to the determination, implementing a Sobol' indices technique or a Kucherenko indices technique to generate global sensitivity analysis results that include property indices for the reservoir properties; generating confidence intervals for the property indices; and generating a graphical user interface for rendering the property indices with the confidence intervals for a number of the reservoir properties.