Subsurface Stress Model Downscaling via Coarse Bulk Segmentation
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Solution Overview
Problem
Current geomechanical simulations using seismic inversion data are time-consuming, requiring several weeks to produce a subsurface stress field model and several months for multiple equally probable models, due to the complexity and high resolution needed.
Innovation Solution
A method is introduced to generate subsurface stress models by creating a coarse geomechanical property model from seismic data, which is then used to produce a bulk stress and/or strain model, combining it with fine scale geomechanical property models to efficiently generate multiple fine scale subsurface stress models, reducing the computational time and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution geomechanical property models are used for subsurface stress field simulation, then prediction accuracy is improved, but computational time increases significantly
Solution Approach 1:
The method segments the computational process into two distinct stages: first generating a low-resolution bulk stress model from a coarse geomechanical property model, then downsampling this bulk model to high resolution using multiple high-resolution property models. This segmentation allows each stage to operate at its optimal resolution, avoiding the need to run full high-resolution simulations from scratch.
Solution Approach 2:
The method performs preliminary computation by generating the bulk stress model at low resolution first, using a simplified coarse geomechanical property model. This preliminary bulk stress model serves as a foundation that captures the overall stress field behavior, which is then refined in the downscaling stage rather than computing everything at high resolution from the beginning.
2Reliability
If multiple equally probable geomechanical property models are analyzed to account for uncertainty, then reliability of prediction is improved, but productivity decreases
Solution Approach 1:
The method separates the uncertainty analysis into two phases: generating multiple high-resolution geomechanical property models to capture different equally probable scenarios, then efficiently downsampling each to generate corresponding high-resolution stress models. This segmentation makes it feasible to process multiple models by reducing the computational burden of each individual stress model generation.
Solution Approach 2:
The method creates copies of the bulk stress model for each high-resolution geomechanical property model, applying the downscaling procedure to generate multiple high-resolution stress models. Rather than running independent full simulations for each scenario, the approach copies and adapts the bulk model results, significantly reducing computational requirements while maintaining the ability to evaluate multiple equally probable outcomes.
Data Source
AI summary
A method for generating one or more subsurface stress models. The method may include receiving seismic data. A plurality of first geomechanical property models may be generated based at least partially on the seismic data. A second geomechanical property model may be generated based at least partially on the seismic data. The second geomechanical property model may have a lower resolution than the first geomechanical property models. A stress model, a strain model, or a combination thereof may be generated based on the second geomechanical property model. One or more subsurface stress models may be generated based on the stress model, the strain model, or the combination thereof and the first geomechanical property models.