Predictive Chance Maps for Petroleum Exploration Uncertainty
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Solution Overview
Problem
Traditional Earth system models used in hydrocarbon exploration fail to account for uncertainties in model inputs, leading to overly precise 'yes' or 'no' predictions about petroleum system elements, which do not reflect the actual precision of the modeling technique and lack consistency with the level of uncertainty present.
Innovation Solution
The method generates predictive chance maps by defining and classifying modelled parameters into data bins with assigned likelihood values, applying parameter weighting factors, and combining initial chance maps to create a final chance map that visually depicts the likelihood of encountering features of interest, such as source rocks or hydrocarbon reservoirs, while accounting for uncertainties like atmospheric carbon dioxide levels and tectonic movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Earth system models generate definitive 'yes' or 'no' predictions, then the prediction precision appears high, but the predictions do not account for uncertainties in model inputs and simulations
Solution Approach 1:
The patent transforms the output format from definitive binary predictions to probabilistic chance maps with multiple likelihood categories (definite, likely, possible, unlikely, impossible). This parameter change in the prediction scale allows the system to maintain visual precision while incorporating uncertainty information through weighted combinations of multiple Earth system model simulations.
Solution Approach 2:
The patent segments the prediction output into distinct likelihood categories and processes multiple independent model simulations separately. By running multiple simulations with different input parameters and combining their results through weighting, the system divides the single definitive prediction into multiple probabilistic outcomes that collectively represent the uncertainty range.
2Ease of operation
If traditional models provide definitive predictions, then the output is simple and clear, but the predictions do not reflect the actual precision permitted by the modeling technique
Solution Approach 1:
The patent changes the output parameter from binary yes/no to a five-category likelihood scale (definite, likely, possible, unlikely, impossible). This maintains ease of interpretation through clear visual categories while achieving consistency with modeling precision by incorporating uncertainty from multiple simulations into the probability assignments.
3Reliability
If multiple Earth system model simulations are run with different inputs, then uncertainty is accounted for, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent merges multiple independent Earth system model simulations into a single integrated chance map output. By combining the results through weighted averaging and aggregating likelihood categories across simulations, the system consolidates complex computational outputs into a unified visual representation that accounts for uncertainty without requiring separate analysis of each simulation.
Data Source
AI summary
A non-transitory computer readable medium includes a set of instructions that in operation cause a processor to determine at least one modelled parameter of a feature of interest in petroleum exploration. The instructions also cause a processor to assign a likelihood value to each modelled parameter of the at least one modelled parameter and to generate an initial chance map for each modelled parameter of the at least one modelled parameter. Further, the instructions cause a processor to assign a weighting factor for each modelled parameter of the at least one modelled parameter, and to combine the initial chance maps using the weighting factor for each modelled parameter of the at least one modelled parameter to generate a first simulation chance map.