Subsurface Data Analysis Using Physics-Based Plausibility Filters
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Solution Overview
Problem
Existing uncertainty analysis methods for subsurface data in the oil and gas industry are ineffective in capturing physically plausible scenarios, often including implausible ones, leading to inaccurate P10 and P90 values due to assumptions of independent parameters and lack of physical connection, resulting in 'surprises' for decision-makers.
Innovation Solution
A physics-based uncertainty analysis method that identifies key controlling parameters and ranges using geologic and stratigraphic analysis, applies experimental design and physics-based modeling to generate digital analogs of subsurface assets, and iteratively refines the parameter space to ensure only physically plausible scenarios are sampled, using techniques like Latin Hypercube Sampling and computational stratigraphy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the ranges of uncertainty parameters are widened to capture important scenarios, then the coverage of plausible scenarios is improved, but the number of implausible scenarios increases disproportionately, leading to false P10, P50 and P90 values
Solution Approach 1:
The patent segments the uncertainty analysis into two distinct phases: (1) generating a broad ensemble of models with wide parameter ranges to ensure coverage of all possible scenarios, and (2) applying physics-based plausibility filters to segment and remove implausible scenarios. This segmentation allows the method to capture important scenarios while eliminating false ones, resolving the contradiction between coverage and accuracy.
Solution Approach 2:
The patent performs preliminary actions by first generating a comprehensive ensemble of digital analogs with wide parameter ranges before applying plausibility filters. This preliminary broad sampling ensures that important scenarios are captured, and then the filtering process removes implausible cases. The preliminary action of wide sampling followed by filtering resolves the contradiction by ensuring both coverage and accuracy.
2Ease of operation
If simple statistical methods are used for uncertainty analysis, then the ease of operation is improved, but the ability to capture physically plausible scenarios deteriorates
Solution Approach 1:
The patent introduces physics-based plausibility filters as intermediaries between the simple statistical sampling process and the final uncertainty analysis results. These filters act as a mediator that maintains the simplicity of Monte Carlo sampling while adding the capability to identify and remove implausible scenarios. The intermediary filtering mechanism resolves the contradiction by preserving ease of operation while improving reliability.
Solution Approach 2:
The patent replaces the purely statistical mechanical system with a hybrid system that incorporates physics-based constraints and plausibility filters. Instead of relying solely on statistical assumptions about parameter independence, the method substitutes physics-based models that explicitly represent subsurface processes. This substitution maintains operational simplicity while dramatically improving the accuracy of scenario capture.
3Measurement precision
If the number of uncertainty parameters is increased to improve model accuracy, then the measurement precision is improved, but the number of physically implausible cases scales exponentially, diminishing the impact of plausible scenarios
Solution Approach 1:
The patent implements feedback mechanisms where physics-based plausibility filters continuously evaluate and remove implausible scenarios from the ensemble. As the number of parameters increases, the feedback filtering process becomes more effective at identifying and eliminating the exponentially growing number of implausible cases. This feedback loop ensures that only physically plausible scenarios contribute to the final P10, P50, and P90 values, resolving the contradiction between model accuracy and the proliferation of implausible cases.
Data Source
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AI summary
A method is described for analysis of subsurface data including the use of physics-based modeling and experimental design that allows calculation of probabilities of physical subsurface properties. The method may include calculations of key controlling parameters. The method may include using multiple dimension scaling. The method may be executed by a computer system.