Phase-Weighted Nth Root Stack for Seismic Polarity Reversals
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Solution Overview
Problem
Current seismic data processing methods face challenges in accurately locating microseismic events due to polarity changes, which result in destructive interference and reduced detection accuracy, especially when signals with opposing polarities cancel each other out during stacking computations.
Innovation Solution
The implementation of a phase-weighted nth root stack method, which computes a phase-weighted stack and an nth root stack as a product, allowing for improved signal-to-noise ratio (SNR) and rendering the resulting stack impervious to polarity reversals, thereby enhancing the detection and location of seismic events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional stacking computations are used to sum seismic signals, then processing simplicity is maintained, but signals with opposing polarities cancel each other out causing destructive interference and reduced detection accuracy
Solution Approach 1:
The patent inverts the conventional stacking approach by using minimum normalization instead of simple summation. This inversion allows signals with opposing polarities to contribute constructively to the stack rather than canceling each other out, thereby improving detection accuracy while maintaining processing feasibility through the use of normalized minimum values.
Solution Approach 2:
The patent changes the stacking parameter from simple arithmetic summation to a normalized minimum-based computation. This parameter change transforms how polarities are handled during stacking, allowing both positive and negative polarity signals to be preserved and utilized effectively, thus resolving the destructive interference problem.
2Measurement precision
If discrete time picks are selected for each seismic detector to locate microseismic events, then event location can be achieved, but accuracy is reduced when picks do not correspond to the same wave arrival or unique event
Solution Approach 1:
The patent extracts the essential timing information through minimum normalization, removing the need for manual discrete time picks. By extracting the minimum value and its associated time from each trace, the method automatically identifies wave arrivals without requiring operator intervention, thereby improving both accuracy and reducing picking difficulty.
Solution Approach 2:
The stacking method performs self-service by automatically identifying and utilizing the minimum amplitude points from each trace. This self-identifying mechanism eliminates the need for external manual picking, allowing the data itself to provide the necessary timing information for accurate event location.
3Reliability
If signals with opposing polarities are summed during stacking, then processing is straightforward, but the resulting stack has reduced signal-to-noise ratio due to destructive interference
Solution Approach 1:
The patent inverts the conventional summation approach by using minimum normalization. Instead of adding signals that may have opposing polarities, the method takes the minimum value across traces, which preserves signals with both positive and negative polarities without causing destructive interference, thereby improving signal-to-noise ratio.
Solution Approach 2:
The patent converts the harmful effect of opposing polarities into a benefit. By using minimum normalization, the method transforms what would traditionally be destructive interference into constructive contribution, allowing signals with opposite polarities to both enhance the final stack rather than cancel each other out.
Data Source
AI summary
Seismic data processing using one or more non-linear stacking enabling detection of weak signals relative to noise levels. The non-linear stacking includes a double phase, a double phase-weighted, a real phasor, a squared real phasor, a phase and an N-th root stack. Microseismic signals as recorded by one or more seismic detectors and transformed by transforming the signal to enhance detection of arrivals. The transforms enable the generation of an image, or map, representative of the likelihood that there was a source of seismic energy occurring at a given point in time at a particular point in space, which may be used, for example, in monitoring operations such as hydraulic fracturing, fluid production, water flooding, steam flooding, gas flooding, and formation compaction.