Adaptive EOR Optimization via Sensitivity Analysis
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Solution Overview
Problem
Enhanced oil recovery (EOR) processes face uncertainty due to unknown formation and fluid properties, leading to unpredictable performance metrics and suboptimal strategies.
Innovation Solution
A method using predictive physics-based reservoir simulations to optimize EOR projects by identifying and reducing uncertainty through global sensitivity analysis, ranking parameters by their contribution to performance metrics, and conducting targeted measurements to refine uncertain properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional recovery mechanisms are used, then operational simplicity is maintained, but oil recovery efficiency remains limited
Solution Approach 1:
The patent performs preliminary sensitivity analysis and uncertainty quantification before implementing the full EOR process. By ranking uncertain parameters and identifying those with highest impact on performance metrics in advance, the methodology prepares the optimization framework proactively, allowing systematic risk management before actual injection operations begin
Solution Approach 2:
The patent implements dynamic adaptive optimization where control variables are continuously adjusted based on reduced uncertainty from measurements. The efficient frontier is recalculated iteratively as uncertainty ranges narrow, allowing the system to adapt its strategy dynamically rather than following a fixed predetermined plan
2Measurement precision
If comprehensive sensitivity analysis is performed on all uncertain parameters, then accuracy of performance prediction is improved, but computational resources and time are excessive
Solution Approach 1:
The patent segments the set of uncertain parameters by ranking them according to their sensitivity indices. Instead of treating all parameters equally, the methodology divides them into tiers based on their impact on performance metrics, focusing computational effort on measuring and reducing uncertainty in only the most influential parameters rather than all parameters uniformly
Solution Approach 2:
The patent performs partial sensitivity analysis by concentrating measurement resources on reducing uncertainty in high-ranking parameters only. Rather than attempting to reduce uncertainty in all parameters to the same level, the methodology accepts higher uncertainty in low-ranking parameters, achieving sufficient prediction accuracy with reduced computational and measurement effort
3Reliability
If uncertainty ranges of formation properties are reduced through additional measurements, then reliability of EOR performance prediction is improved, but measurement and pilot costs increase
Solution Approach 1:
The patent changes the state of uncertain parameters by reducing their uncertainty ranges through targeted measurements. The methodology systematically narrows the probability distribution ranges of ranked parameters based on their sensitivity indices, transforming high-uncertainty parameters into better-constrained parameters through selective data acquisition and pilot testing
4Productivity
If optimal control variables are determined under initial uncertainty, then EOR performance is maximized, but risk of achieving desired performance remains high
Solution Approach 1:
The patent implements feedback by iteratively updating the reservoir model with reduced uncertainty information from measurements. The efficient frontier is recalculated in subsequent iterations as uncertainty ranges narrow, allowing the optimization to incorporate new information and adjust control variables accordingly, creating a closed-loop system that reduces risk through continuous learning and adaptation
Data Source
AI summary
Methods are provided for adaptive optimization of enhanced oil recovery project performance under uncertainty. Predictive physics-based reservoir simulation is used to estimate performance of the project. Input parameters of the model are divided into control variables and uncertain variables. The reservoir model is optimized to obtain values of control variables maximizing mean value of a chosen performance metric under initial uncertainty of formation and fluid properties. An efficient frontier can characterize dependence between the optimized mean value of the performance metric and its uncertainty expressed by the standard deviation. Global sensitivity analysis (GSA) is then applied to quantify and rank contributions from uncertain input parameters to the standard deviation of the optimized values of the performance metric. Additional measurements can be performed to reduce uncertainty in the high-ranking parameters. Constrained optimization of the model with reduced ranges of uncertain parameters is performed and a new efficient frontier is obtained.