Subsidence Map Calibration via Iterative Residual Minimization
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Solution Overview
Problem
Accurately calibrating subsidence maps is challenging due to the complex interplay between subsidence mechanisms, which affects the reliability of stratigraphic model predictions in the oil and gas industry for hydrocarbon resource attributes.
Innovation Solution
A method involving a stratigraphic model and subsidence map calibration using iterative updates of variable values, determined by inputting the subsidence map into the stratigraphic model and calculating residuals with an objective function, until the residual falls below a defined threshold, employing either the pilot points or weighted linear combination methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative calibration of subsidence map is performed to improve prediction accuracy, then reliability of stratigraphic model predictions is improved, but time and computational resources required are increased
Solution Approach 1:
The patent applies parameter changes by systematically adjusting subsidence map variable values through iterative calibration. The objective function evaluates model outputs against target outputs and guides parameter adjustments, allowing the subsidence map parameters to evolve toward optimal values that improve prediction reliability while managing calibration time through automated optimization.
Solution Approach 2:
The patent implements feedback through the objective function that continuously compares model outputs with target outputs (such as seismic data or well log data). This feedback loop guides the iterative calibration process by providing directional information for adjusting subsidence map parameters, ensuring that each iteration moves the model closer to accurate predictions without requiring exhaustive manual trial-and-error.
2Measurement precision
If complex subsidence mechanisms are fully modeled to improve measurement precision, then accuracy of subsidence measurement is improved, but device complexity and computational requirements are increased
Solution Approach 1:
The patent manages model complexity through parameter changes by focusing calibration efforts on the most influential subsidence map parameters. The objective function identifies which parameters have the greatest impact on model outputs, allowing the system to refine only those critical parameters while keeping less influential parameters at default values, thus maintaining measurement precision without requiring full complexity in all model aspects.
Solution Approach 2:
The patent applies partial action by implementing selective calibration of specific subsidence map parameters rather than attempting to optimize all parameters simultaneously. This approach focuses computational resources on the most critical parameters that have the greatest impact on prediction accuracy, achieving sufficient measurement precision without the prohibitive complexity of comprehensive full-parameter optimization.
Data Source
AI summary
Methods and systems for calibrating a subsidence map are disclosed. The method includes selecting a stratigraphic model that represents a geological formation and defining the subsidence map with a first set of variable values. The method further includes obtaining target outputs measured from the geological formation. The method still further includes determining first model outputs from the stratigraphic model by inputting the subsidence map with the first set of variable values into the stratigraphic model and determining a first residual between the target outputs and the first model outputs using an objective function.