Stratigraphic Forward Model Uncertainty Quantification
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Solution Overview
Problem
Current stratigraphic modeling techniques face challenges in constraining input parameters due to sparse and uncertain validation data, leading to unreliable simulation results, especially in frontier explorations where data is limited and uncertain.
Innovation Solution
A unified input/output technique that calculates probability distributions for both validation data and forward model input parameters, allowing for the selection of simulation model results that are most consistent with all available data, including geologically reasonable parameter ranges, to produce a globally optimized analysis and realistic uncertainty assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional stratigraphic modeling techniques are used with sparse validation data, then data collection costs are reduced, but the reliability and precision of simulation results deteriorate
Solution Approach 1:
The patent transforms the modeling approach by changing from deterministic parameter input to probabilistic parameter distribution input. By representing input parameters as probability distributions rather than fixed values, the model can quantify and propagate uncertainty through the simulation, producing statistically robust results even with sparse validation data.
Solution Approach 2:
The patent implements a feedback mechanism where simulation outputs are compared against available validation data to calculate likelihood values. This feedback loop allows the model to assess how well different parameter sets explain the observed data, enabling selection of the most probable geological scenarios without requiring extensive additional data collection.
2Measurement precision
If more validation data is collected to improve model accuracy, then simulation result precision improves, but the cost and time of additional testing increase
Solution Approach 1:
The patent applies partial action by utilizing only the sparse validation data that is already available, rather than requiring complete or excessive data collection. The probabilistic framework allows the model to work effectively with incomplete information by explicitly modeling and propagating the uncertainty associated with the limited data set.
Solution Approach 2:
The patent performs preliminary probabilistic analysis using existing sparse data before committing to expensive additional testing. By calculating likelihood values and identifying most probable parameter sets upfront, the method enables decision-makers to assess model reliability early in the exploration process, potentially avoiding unnecessary additional data collection.
3Device complexity
If deterministic input parameters are used in forward modeling, then the model complexity is reduced, but the ability to assess uncertainty deteriorates
Solution Approach 1:
The patent adds a probabilistic dimension to the traditional deterministic modeling approach. By transforming fixed parameter values into probability distributions and incorporating likelihood calculations based on validation data, the model gains the capability to assess uncertainty and quantify the reliability of simulation results without substantially increasing operational complexity.
Data Source
Figure 1A
Figure 1B~1E
Figure 1F~1G
AI summary
Disclosed is a method and system for identifying simulated basin results and associated input parameter values for simulation of geographic basins by a stratigraphic forward model simulation program that are most likely to represent the actual basin by treating inputs and outputs of the stratigraphic forward model simulation program in a unified manner. An embodiment may calculate probability distributions for input parameters and validation data, and calculate likelihoods of simulated basins as a combination of the combination of the probabilities of the input parameters used to create the simulated basin and of the combination of the probabilities simulation validation results of the simulated basin. An embodiment may then select most likely simulation model result basins based on the results having a higher calculated likelihood. The most likely simulated basins may be used for analysis of exploration and/or production decisions without the need for additional, expensive testing on the actual basin.