Stratigraphic Model Calibration via Iterative Seismic Inversion
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Solution Overview
Problem
Geological boundary conditions, especially those associated with the past, present great uncertainty in forward stratigraphic modeling, leading to propagation of uncertainty in stratigraphic models.
Innovation Solution
A method involving seismic inversion to determine an inverted value of a stratum parameter, using well datasets to determine a standard deviation, and iteratively defining geological boundary conditions to calculate a misfit value until it is below a tolerance value, resulting in a calibrated stratigraphic model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If forward stratigraphic modeling is performed using physics-based equations with geological boundary conditions, then insight into hydrocarbon reservoir presence is improved, but uncertainty in past geological boundary conditions propagates through the model reducing reliability
Solution Approach 1:
The patent implements an iterative feedback loop where the stratigraphic model output is compared against actual seismic data, and the geological boundary conditions are adjusted based on this comparison. The forward model generates predicted seismic responses, which are then compared to observed seismic data, and the boundary conditions are refined in subsequent iterations to minimize misfit, thereby reducing uncertainty propagation.
Solution Approach 2:
The patent performs preliminary seismic inversion on the seismic dataset before running the forward stratigraphic model. This preliminary action extracts key stratigraphic parameters and boundary condition constraints from the seismic data, which are then used to guide the forward modeling process, reducing the search space and improving model reliability from the outset.
2Measurement precision
If iterative definition of geological boundary conditions is performed to reduce uncertainty, then model accuracy is improved, but computational time and process complexity increase
Solution Approach 1:
The patent applies partial action by performing seismic inversion on selected key stratigraphic parameters rather than the entire dataset in each iteration. By focusing computational effort on the most influential boundary conditions and stratigraphic parameters, the method achieves adequate precision without requiring exhaustive computation on all possible parameters.
Solution Approach 2:
The patent performs preliminary seismic inversion to extract initial estimates of stratigraphic parameters and boundary conditions before the iterative forward modeling process. This preliminary action provides starting values that converge faster during iteration, reducing the total computational time required to achieve the desired measurement precision.
3Measurement precision
If seismic inversion is applied to determine stratum parameters, then measurement precision of stratum parameters is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the seismic inversion process into discrete, manageable steps: preprocessing the seismic dataset, applying inversion algorithms to extract specific stratigraphic parameters, and integrating results into the forward model. This segmentation allows the complex inversion process to be implemented using standard computational tools and methodologies.
Solution Approach 2:
The patent uses the forward stratigraphic model as an intermediary between the seismic inversion results and the final calibrated model. The inversion provides initial parameter estimates, the forward model tests these parameters against geological physics, and the cycle repeats, allowing the complex inversion problem to be solved through iterative refinement rather than direct complex computation.
Data Source
AI summary
Systems and methods are disclosed. The method includes obtaining a seismic dataset for a subterranean region of interest and determining an inverted value of a stratum parameter by applying seismic inversion to the seismic dataset. The method further includes obtaining a well dataset within the subterranean region of interest and determining a standard deviation of the stratum parameter using the well dataset. The method still further includes iteratively or recursively defining a geological boundary condition for the subterranean region of interest, determining a stratigraphic model by applying forward stratigraphic modeling using the geological boundary condition, determining a measured value of the stratum parameter by applying a rock physics model to the stratigraphic model, and calculating the misfit value using the inverted value, the standard deviation, and the measured value until a misfit value is below a tolerance value. The stratigraphic model is then selected as a calibrated stratigraphic model.