Seismic Surface Pick Quality Assessment via Wavelet Comparison
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Solution Overview
Problem
Current seismic exploration methods for hydrocarbons lack effective tools for globally assessing seismic surface pick quality and detecting geological and depositional changes, making it difficult to identify and target hydrocarbon deposits accurately.
Innovation Solution
A system that generates custom seismic surface and volume attributes by analyzing 3D seismic data, comparing wavelets to a reference model, and using weight functions to quantify pick quality and highlight stratigraphic features, allowing for a global assessment of seismic surface pick quality and identification of similar geological settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seismic exploration methods are used, then basic hydrocarbon detection is possible, but the ability to globally assess seismic surface pick quality and detect geological changes is insufficient
Solution Approach 1:
The patent segments the seismic data into individual wavelets at each spatial coordinate along the seismic surface. Each wavelet is then independently analyzed and compared to a reference wavelet, allowing for localized quality assessment while maintaining global coverage. This segmentation enables precise measurement of pick quality at each point without overwhelming complexity.
Solution Approach 2:
The patent introduces a reference wavelet as an intermediary standard against which all extracted wavelets are compared. This reference wavelet serves as a mediator that translates complex seismic data into measurable quality metrics, making it possible to objectively assess pick quality and detect geological changes without direct observation of the subsurface conditions.
2Reliability
If global assessment of seismic surface pick quality is implemented, then hydrocarbon prospecting accuracy improves, but computational complexity increases
Solution Approach 1:
The patent implements a self-service approach where the system automatically extracts wavelets, compares them to the reference wavelet, and generates quality assessments without requiring manual intervention. The computational system serves itself by automatically processing the entire seismic cube and producing surface attribute maps, reducing the need for human expertise in each calculation step while maintaining high reliability.
Solution Approach 2:
The patent transforms the complex seismic data into simplified parameters through wavelet extraction and comparison. By converting the original seismic traces into wavelet parameters (amplitude, frequency, phase) and then into quality metrics, the system reduces computational complexity while preserving the essential information needed for reliable hydrocarbon prospecting accuracy.
3Measurement precision
If wavelet comparison to reference model is used, then pick quality quantification is achieved, but detection of local variations becomes more difficult
Solution Approach 1:
The patent applies local quality by extracting and analyzing wavelets at each individual spatial coordinate along the seismic surface. The comparison is performed locally at each point, allowing the system to detect and quantify pick quality variations specific to each location. This localized approach enables the system to identify areas with different geological characteristics while maintaining overall global assessment capability.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to generate a custom seismic surface and volume attribute. In one aspect, a method includes receiving a seismic cube and a seismic surface, and the seismic cube includes traces recorded at receivers deployed to collect seismic data. The seismic surface is picked on the seismic cube. Seismic wavelets are extracted with a selected length from the seismic cube along an intersection with the seismic surface for each spatial coordinate associated with the seismic surface. A reference wavelet is determined. A surface attribute map is generated based on comparing each of the seismic wavelets to the reference wavelet. A productivity of the seismic surface is evaluated using the surface attribute map.