Reservoir Ensemble Simulation for Uncertainty Quantification
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Solution Overview
Problem
Current methods for simulating subterranean environments, such as petroleum reservoirs, face challenges in accurately modeling and predicting fluid flow and hydrocarbon production due to uncertainties in input data and computational resources, leading to inefficient exploration and production strategies.
Innovation Solution
The method involves generating an ensemble of simulation models with varying input parameters, performing simulations to determine statistically representative models, and iteratively refining the ensemble based on confidence metrics to optimize model accuracy and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single deterministic model is used for reservoir simulation, then computational resources and time are conserved, but the ability to capture uncertainties and provide statistically representative predictions is reduced
Solution Approach 1:
The patent creates multiple copies of the reservoir model (ensemble members) with varied input parameters to represent different possible realizations of subsurface uncertainty. Instead of relying on a single deterministic model, the system generates numerous model copies that collectively capture the range of possible outcomes, improving prediction reliability through statistical analysis of the ensemble.
2Reliability
If an ensemble of simulation models is generated to capture uncertainties, then prediction accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements adaptive ensemble sizing that determines the optimal number of model realizations needed to achieve convergence of key metrics. Rather than running an excessive number of simulations indefinitely, the system monitors metric stability and terminates model generation when sufficient statistical representation is achieved, balancing prediction accuracy with computational efficiency.
Solution Approach 2:
The system continuously monitors indexed metrics from simulation outputs and uses this feedback to determine whether additional ensemble members are needed. When metrics converge or reach stability thresholds, the feedback mechanism signals that the ensemble is sufficiently representative, preventing unnecessary computational expenditure on additional model realizations.
3Loss of information
If multiple simulation models with varying parameters are performed, then uncertainty characterization improves, but the complexity of data processing and metric determination increases
Solution Approach 1:
The patent replaces complex manual analysis of ensemble variability with automated computational metrics. Instead of manually assessing uncertainty from multiple models, the system computes indexed metrics that automatically quantify ensemble spread and convergence, substituting mechanical processing complexity with systematic algorithmic evaluation that efficiently characterizes uncertainty.
Data Source
AI summary
A method can include generating an initial number of simulation models of an environment, where each of the initial number of simulation models includes a corresponding input set of values for parameters; generating an additional number of simulation models of the environment, where each of the additional number of simulation models includes a corresponding input set of values for the parameters; performing simulations of physical phenomena using each of the initial number of simulation models and each of the additional number of simulation models, where each of the simulations generates a corresponding indexed output set of values; determining a series of indexed metrics using the indexed output sets; and, based on the series of indexed metrics, deciding to output the initial number of simulation models and the additional number of simulation models as an ensemble statistically representative of the environment or to generate one or more additional simulation models.