Stochastic Geothermal Reservoir Power Estimation
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Solution Overview
Problem
Current subsurface geothermal reservoir models are limited in accurately predicting electrical power generation and rare earth element extraction due to uncertainty in reservoir parameters and the need for external estimates, lacking integrated stochastic modeling and economic analysis.
Innovation Solution
A system and method utilizing a stochastic model executed by a Monte Carlo algorithm to quantify uncertainty in geothermal reservoir parameters, coupled with an economic analyzer to estimate electrical energy potential and rare earth element extraction, incorporating reservoir simulators and analytical models for enhanced geothermal systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reservoir simulation models with volumetric information are used to estimate heat energy potential, then the geological characterization is improved, but external assessment is still required which increases system complexity and reduces prediction accuracy
Solution Approach 1:
The patent combines reservoir simulation models, stochastic modeling, and electricity generation prediction into a single integrated system. The reservoir simulator outputs are directly fed into the stochastic model which then predicts electrical energy potential, eliminating the need for separate external assessments and improving prediction accuracy while reducing system complexity.
Solution Approach 2:
The integrated system performs multiple functions: it characterizes reservoir geology, simulates fluid flow and heat transfer, quantifies uncertainty through stochastic modeling, and predicts electricity generation potential all within one framework. This multi-functional approach replaces the need for multiple separate assessment tools.
2Reliability
If stochastic modeling with Monte Carlo algorithms is implemented to quantify uncertainty, then the reliability of predictions is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary stochastic modeling using Monte Carlo simulations to establish probability distributions for key reservoir parameters before running the main electricity generation prediction. This preliminary characterization of uncertainty allows the model to efficiently sample from pre-defined distributions rather than performing full uncertainty analysis at each prediction step.
Solution Approach 2:
The stochastic model focuses computational effort on the most critical uncertain parameters that have the greatest impact on electricity generation predictions. Rather than equally analyzing all possible uncertainties, the system identifies and prioritizes key parameters such as reservoir temperature, fluid flow rates, and rock properties for detailed stochastic analysis.
3Adaptability or versatility
If integrated economic analysis is incorporated to estimate electrical energy and rare earth element extraction, then the economic planning capability is improved, but the model complexity and data requirements increase
Solution Approach 1:
The economic analysis module is segmented into distinct components: one for estimating electrical energy potential from geothermal reservoirs and another for rare earth element extraction. Each segment uses the stochastic model outputs independently, allowing the system to provide specialized economic assessments without requiring complete re-analysis for each application.
Solution Approach 2:
The stochastic model acts as an intermediary between the reservoir simulation and economic analysis components. It translates geological and engineering parameters into probabilistic distributions that can be directly used by economic evaluation modules, bridging the gap between technical reservoir characterization and economic decision-making.
Data Source
AI summary
The present disclosure is related to systems and/or computer-implemented methods that can estimate an amount of electrical power that can be generated from a geothermal subsurface reservoir. One or more embodiments described herein can include a system, which can comprise a memory to store computer executable instructions. The system can also comprise one or more processors, operatively coupled to the memory, which can execute the computer executable instructions to implement a stochastic model configured to execute a Monte Carlo algorithm that quantifies uncertainty associated with parameters characterizing a geothermal subsurface reservoir. The stochastic model can be further configured to estimate an amount of electrical power associated with the geothermal subsurface reservoir based on the parameters. Additionally, the computer executable instructions can comprise an economic analyzer that generates determines an of hydrocarbon fuel required to produce the amount of electrical power.