Fault Block Merging Sequence for Complex Fault Networks
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Solution Overview
Problem
Existing methods for unfaulting complex fault-network topologies face challenges in determining the optimal sequence of fault blocks to merge, especially in interlocking or cross-faulted topologies, which can lead to geometric failures and unsuccessful unfaulting solutions.
Innovation Solution
A method involving a flowchart-based algorithm that iteratively chooses fault blocks for merging, using a matching factor calculation based on angle, overlap ratio, and overlapped length to select pairs with the highest matching factor, ensuring a systematic and recursive unfaulting process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods are used to unfault complex fault-network topologies, then the process may be simpler, but geometric failures occur and unfaulting solutions are unsuccessful
Solution Approach 1:
The algorithm segments the complex fault-network topology into manageable components by identifying and processing specific fault block pairs based on geometric criteria. The matching factor calculation divides the problem into discrete evaluatable units (angle, overlap ratio, overlapped length) that can be systematically assessed and processed.
Solution Approach 2:
The algorithm performs preliminary actions by pre-calculating geometric parameters (angle, overlap ratio, overlapped length) for all fault block pairs before the actual unfaulting sequence is executed. This preliminary assessment allows the system to identify the optimal merging sequence in advance, avoiding geometric failures during the unfaulting process.
2Manufacturing precision
If a systematic matching factor calculation is used to select fault block pairs, then unfaulting accuracy is improved, but computational time increases
Solution Approach 1:
The matching factor calculation is segmented into three independent geometric components (angle, overlap ratio, overlapped length) that can be calculated separately and then combined. This segmentation allows for efficient computation of each parameter independently, reducing the overall computational burden while maintaining high precision in the final matching factor assessment.
Solution Approach 2:
The algorithm transforms the complex unfaulting decision problem into a quantitative parameter-based selection process. By changing the approach from qualitative geometric assessment to quantitative parameter calculation (angle, overlap ratio, overlapped length), the system achieves high precision in fault block pair selection while enabling efficient computational comparison of multiple candidates.
Data Source
AI summary
In various examples, a method includes storing one or more data structures on a storage device, the one or more data structures identifying a plurality of faults in a geographical formation and a plurality of fault blocks on either side of the plurality of faults in the geographic formation; for each pair of faults blocks on opposite sides of a fault identified in the one or more data structures: determining, using at least one processor, a fault polygon of a respective pair of fault blocks with respect to a fault of the plurality of faults; and calculating a matching factor between the respective pair of fault blocks based on the fault polygon; selecting a pair of fault blocks to merge based on the calculated matching factor; and updating the one or more data structures to indicate the selected pair of fault blocks has been merged.