Geological Stress Inversion via Fault Displacement and Slip Tendency
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Solution Overview
Problem
Conventional stress inversion methods rely on limited availability of slip vector data, which is not readily accessible from seismic reflection data and microseismic swarms commonly used in the oil and gas industry, necessitating a more robust approach for estimating stress states responsible for observed faults.
Innovation Solution
The method utilizes fault displacement data as a source, incorporating slip surface orientations and displacements, along with proxies like fault area and earthquake focal mechanisms, to invert the stress tensor, constraining it to geologically realistic values and calculating an error measure to minimize the estimation of the stress state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional stress inversion methods use slip vector data, then stress state estimation accuracy is improved, but data availability deteriorates because slip vector data is not readily accessible from seismic reflection data and microseismic swarms
Solution Approach 1:
The patent introduces slip tendency as an intermediary parameter that bridges the gap between available fault displacement data and stress state estimation. Instead of directly using slip vector data which is unavailable, the method calculates slip tendency from fault orientation and stress tensor, then inverts the stress tensor by matching calculated slip tendency with observed fault displacement patterns. This intermediary approach enables stress inversion using readily available seismic and microseismic data.
Solution Approach 2:
The patent changes the input parameter from slip vector (unavailable) to fault displacement (available), and transforms the inversion approach by using slip tendency ratios as the connecting parameter. The method reformulates the stress inversion problem to use fault displacement data combined with slip tendency calculations, fundamentally changing the parameter set from direct slip vectors to derived slip tendency metrics that can be computed from available data.
2Quantity of substance
If fault displacement data is used as source data, then data accessibility is improved, but measurement precision of stress state estimation may deteriorate due to data density variations
Solution Approach 1:
The patent applies error measure minimization as a partial action to compensate for data density variations. By calculating an error measure between observed fault displacements and displacements predicted from the inverted stress tensor, the method iteratively adjusts the stress tensor to minimize this error. This partial correction approach handles cases where fault displacement data may be sparse or unevenly distributed, improving precision without requiring complete data coverage.
3Measurement precision
If slip tendency analysis is applied to characterize fault slip, then stress regime characterization is improved, but device complexity increases due to the need to calculate shear stress and normal stress ratios
Solution Approach 1:
The patent makes the slip tendency calculation multi-functional by using the same slip tendency framework for both normal and reverse faulting regimes. The method calculates slip tendency as the ratio of shear stress to normal stress on fault planes, and this universal approach works across different stress regimes (normal, reverse, strike-slip) without requiring separate calculation methods, thereby managing complexity while maintaining broad applicability.
Data Source
AI summary
A computer-implemented method of determining the stress state associated with a geological fault is described. The source data comprises measured fault displacement values, or proxy displacement values, associated with the fault. An error function calculates error as a function of normalized fault displacement and normalized slip tendency. Candidate stress states are used to determine slip tendency values, which are used in the error function to calculate an error value. This value is minimized to determine the “best” candidate stress state.