Reservoir Simulation Dynamic Parameter Optimization
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Solution Overview
Problem
Reservoir simulations require significant computing resources and increasing the number of processing units does not proportionally decrease computing time, leading to inefficiencies and high costs.
Innovation Solution
A reservoir simulation system that dynamically optimizes performance by determining the variance of computation time and applying either a first or second sequence of Bayesian Optimizations to internal and external parameters, such as the number of processors, to reduce computation time and energy utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If the number of processing units is increased to run reservoir simulations, then the computing capacity is improved, but the computing time does not proportionally decrease and costs increase
Solution Approach 1:
The system dynamically adjusts simulation parameters and processing configurations during execution based on real-time performance monitoring. The variance of computation time is calculated and used to adaptively modify simulation settings, allowing the system to optimize computing efficiency without linearly increasing processing units
Solution Approach 2:
The invention changes physical or operational parameters of the simulation system by applying Bayesian Optimization to adjust internal and external parameters. This allows the system to improve computing performance by optimizing parameter configurations rather than simply adding more processing units
2Productivity
If more processing units are allocated to reservoir simulations, then the simulation throughput is improved, but the energy consumption increases significantly
Solution Approach 1:
The system implements feedback mechanisms by monitoring computation time variance and using this information to adjust processing allocations. Bayesian Optimization provides feedback loops that continuously refine parameter settings based on observed performance, enabling the system to maintain high throughput while minimizing energy consumption through adaptive resource management
3Speed
If the number of processing units is increased to reduce computation time, then the simulation speed is improved, but the cost-effectiveness deteriorates
Solution Approach 1:
The system applies partial optimization by focusing computational resources on the most critical simulation components identified through Bayesian Optimization. Rather than uniformly increasing resources across all simulation aspects, the system applies targeted optimizations that achieve significant speed improvements with minimal additional cost
Data Source
AI summary
The disclosed embodiments include reservoir simulation systems and methods to dynamically improve performance of reservoir simulations. The method includes obtaining input variables for generating a reservoir simulation of a reservoir, and generating the reservoir simulation based on the input variables. The method also includes determining a variance of computation time for processing the reservoir simulation. In response to a determination that the variance of computation time is less than or equal to a threshold, the method includes performing a first sequence of Bayesian Optimizations of at least one of internal and external parameters that control the reservoir simulation to improve performance of the reservoir simulation. In response to a determination that the variance of computation time is greater than the threshold, the method includes performing a second sequence of Bayesian Optimizations of at least one of the internal and external parameters.