Seismic Fault Detection Using Coherence Cube Edge Analysis
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Solution Overview
Problem
Current seismic data processing techniques face challenges in accurately and efficiently identifying faults in large datasets, especially in the presence of noise, which hinders the interpretation of seismic data and the identification of potential hydrocarbon reservoirs.
Innovation Solution
The method involves calculating a coherence cube of seismic data, applying a threshold to identify continuity areas, detecting edges and fault points, creating fault segments, and joining them into fault lines using geological and geometrical constraints, while utilizing distributed computing for parallel processing to handle large datasets efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual fault interpretation is performed on large seismic datasets, then interpretation accuracy can be maintained through expert analysis, but the time and labor required become prohibitively excessive
Solution Approach 1:
The system performs automatic fault detection and identification without requiring manual interpretation of every seismic trace. The coherence cube calculation and edge detection algorithms autonomously identify fault candidates, ranking them by confidence value, thereby eliminating the need for extensive manual labor while maintaining interpretation quality
Solution Approach 2:
Manual mechanical interpretation processes are replaced with automated computational algorithms. The system uses coherence-based edge detection and confidence ranking algorithms to substitute human analysts, dramatically reducing interpretation time while maintaining or improving accuracy through consistent application of detection criteria
2Reliability
If noise reduction processing is applied to seismic data, then data quality improves, but the processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary coherence cube calculation on the raw seismic data before fault detection. By pre-processing the data into a coherence cube that highlights continuous reflectors, the system prepares the data for efficient edge detection and fault identification, reducing the need for extensive subsequent noise reduction processing
Solution Approach 2:
The system accepts and works with noisy seismic data directly, converting the presence of noise into a manageable challenge through confidence-based ranking. Rather than attempting to completely eliminate noise through lengthy processing, the system detects fault candidates and ranks them by confidence, allowing interpreters to focus on high-confidence results while accepting that some noise remains in the data
3Manufacturing precision
If detailed fault interpretation is performed across entire large seismic datasets, then comprehensive fault coverage is achieved, but the task becomes impossible to complete in reasonable timeframes
Solution Approach 1:
The system segments the large seismic dataset into manageable processing units and identifies fault candidates independently across the entire volume. By dividing the interpretation task into discrete detection operations that can be performed algorithmically across segments, the system achieves comprehensive coverage without requiring sequential manual interpretation of every area
Solution Approach 2:
The system changes the interpretation approach from detailed manual tracing to automated parameter-based detection using coherence thresholds and confidence values. This parameter-driven approach allows rapid processing of entire datasets by evaluating numerical criteria rather than performing detailed visual analysis, thereby achieving comprehensive fault coverage with high productivity
Data Source
AI summary
Methods and apparatus are provided for automatically identifying possible faults in large seismic datasets. An exemplary method comprises obtaining the seismic data; calculating a coherence cube of the seismic data; performing the following steps for a plurality of two-dimensional seismic sections of the coherence cube: (i) applying a threshold to the coherence cube to obtain a binary image representation comprising continuities; (ii) identifying edges of continuity areas in the binary image representation to identify changes in the continuities as fault point candidates; (iii) identifying fault points in the obtained seismic data based on a fault confidence value indicating a likelihood that a given point is part of a fault; (iv) creating one or more fault segments from the identified fault points; and (v) joining fault segments into fault lines using geological and/or geometrical constraints; and generating three-dimensional fault surfaces from the fault lines in the plurality of two-dimensional seismic sections. The exemplary automatic fault detection method can be parallelized.