Iterative Stochastic Seismic Inversion for Reservoir Characterization
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Solution Overview
Problem
Existing stochastic inversion techniques for reservoir characterization are prone to errors due to user-selected transition probability matrices (TPMs), which can introduce inaccuracies in pseudo well generation, affecting well planning, reserve estimation, and reservoir modeling.
Innovation Solution
The implementation of One Dimensional Stochastic Inversion (ODiSI) method, which matches stochastically simulated 1D stratigraphic profiles to actual seismic traces, and iteratively refines the TPM to reduce subjectivity and errors, using actual seismic data to validate pseudo wells and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user-selected transition probability matrices are used in stochastic inversion, then pseudo wells can be generated, but inaccuracies and errors are introduced in reservoir property estimates
Solution Approach 1:
The patent implements an iterative feedback loop where seismic data is used to update and refine the transition probability matrix. The process involves: (1) generating pseudo-wells using an initial TPM, (2) comparing pseudo-well properties against actual seismic data, (3) using the discrepancies to update the TPM through iterative inversion, and (4) repeating until convergence. This feedback mechanism progressively reduces inaccuracies in reservoir property estimates while maintaining pseudo-well generation capability.
Solution Approach 2:
The patent dynamically changes the parameters of the transition probability matrix through iterative refinement. The TPM is updated based on the comparison between pseudo-well properties and actual seismic measurements, transforming it from a static user-selected matrix into a dynamic, data-driven parameter that adapts to the specific reservoir conditions being modeled.
2Measurement precision
If transition probability matrices are iteratively refined using seismic data, then accuracy of reservoir property estimates improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the inversion process into discrete iterative steps: (1) generate pseudo-wells from well data using current TPM, (2) compute property estimates from pseudo-wells, (3) compare with actual seismic data, (4) update TPM based on discrepancies, and (5) repeat until convergence. This segmentation allows the complex iterative refinement to be managed as a series of manageable operations, each building upon the previous results systematically.
3Adaptability or versatility
If limited well data is used to generate pseudo wells, then exploration of unsampled areas is enabled, but accuracy of pseudo well representation decreases
Solution Approach 1:
The patent uses seismic data as an intermediary to bridge the gap between limited well data and the need to model unsampled areas. The seismic data provides independent constraints that validate and refine the pseudo-well representations. By comparing pseudo-well properties against seismic measurements, the system can adjust the TPM to better represent the true subsurface conditions in areas where direct well data is unavailable, thereby improving accuracy while maintaining the ability to explore unsampled regions.
Data Source
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AI summary
A method includes receiving a first transition probability matrix (TPM) of a subsurface region, wherein the TPM defines, for a given lithology at a current depth sample (or micro-layer), a probability of particular lithologies at a next depth sample (or micro-layer), receiving seismic data for the subsurface region, utilizing the first TPM and the seismic data to generate first pseudo wells, calculating a second TPM from the first pseudo wells, determining whether the second TPM is consistent with the first TPM, and utilizing the first pseudo wells to characterize a reservoir in the subsurface region when the second TPM is determined to be consistent with the first TPM.