Bayesian Inversion for Reservoir Model Uncertainty
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Solution Overview
Problem
Current methods for history matching in oil and gas reservoirs are time-consuming, prone to personal bias, and computationally expensive, especially when dealing with geologically complex reservoirs and nonlinear problems, due to the need for manual processes and high computational costs in simulating multiple model realizations.
Innovation Solution
A computer-implemented method using Bayesian formulations and Monte Carlo Markov chain algorithms for updating geological models, integrating streamline simulations and seismic data to improve data inversion and uncertainty management, reducing computational effort and increasing acceptance rates through efficient model parametrization and ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual history matching methods are used, then personal bias is reduced through expert judgment, but the process becomes time-consuming and computationally expensive
Solution Approach 1:
The patent replaces manual mechanical history matching processes with automated computer-implemented Bayesian inversion algorithms. The system uses probabilistic frameworks and Monte Carlo simulations to automatically update reservoir models, eliminating the need for time-consuming manual iterative adjustments while maintaining or improving model accuracy through rigorous statistical methods.
Solution Approach 2:
The patent transforms the history matching process by changing from deterministic parameter adjustment to probabilistic parameter estimation. By implementing Bayesian inversion that simultaneously updates multiple reservoir parameters (permeability, porosity, saturation) with uncertainty quantification, the system achieves faster convergence while providing comprehensive model uncertainty assessment that manual methods cannot deliver.
2Measurement precision
If diverse geological realizations are selected to account for uncertainties, then forecast statistics accuracy improves, but computational cost increases significantly
Solution Approach 1:
The patent performs preliminary Bayesian inversion and uncertainty quantification before full reservoir simulation. By first identifying the most probable geological realizations and their parameter distributions through efficient probabilistic algorithms, the system pre-screens models to avoid computationally expensive full simulations of all possible realizations, thereby reducing overall computational cost while maintaining forecast accuracy.
Solution Approach 2:
The patent implements a staged approach where Bayesian inversion provides partial information about model uncertainties, which then guides subsequent selective full-physics simulations. Instead of simulating all geological realizations equally, the system performs partial simulations only on the most probable models identified through the Bayesian framework, achieving sufficient forecast accuracy with reduced computational effort.
3Reliability
If Bayesian sampling methods are used, then statistical rigor and accuracy improve, but computational costs become prohibitively high due to high rejection rates
Solution Approach 1:
The patent introduces an intermediary efficient probabilistic algorithm that bridges the gap between rigorous Bayesian sampling and computational efficiency. The system uses streamlined inversion techniques and surrogate models as intermediaries to pre-evaluate candidate realizations, thereby reducing the rejection rate in subsequent full Bayesian sampling steps and improving overall computational productivity while maintaining statistical rigor.
4Manufacturing precision
If full-physics reservoir simulation is performed for every model realization, then model accuracy improves, but computational time and costs become prohibitive
Solution Approach 1:
The patent segments the reservoir modeling process into distinct stages: (1) Bayesian inversion for rapid probabilistic parameter estimation, (2) uncertainty quantification through streamlined simulations, and (3) selective full-physics simulation only for the most probable models. This segmentation allows the system to achieve adequate model accuracy through efficient methods for most realizations, reserving computationally intensive full simulations only where necessary.
Solution Approach 2:
The patent uses simplified surrogate models and streamlined reservoir simulations as copies or approximations of full-physics simulations. These computationally efficient surrogate models replicate the essential behavior of complex reservoir systems, enabling rapid evaluation of multiple geological realizations without the prohibitive computational cost of running complete full-physics simulations on every model.
Data Source
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AI summary
Systems and methods for updating posterior geological models by integrating various reservoir data to support dynamic-quantitative data-inversion, stochastic-uncertainty-management and smart reservoir-management.