Seismic Surface Quality Assessment via Wavelet Cross-Correlation
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Solution Overview
Problem
Current methods for assessing seismic surface quality in geo-exploration are manual, prone to human error, and fail to consistently track seismic events due to noise and inadequate signal processing, leading to inconsistent and unreliable surface interpretations.
Innovation Solution
A computer-implemented method that accesses seismic traces, extracts wavelets of varying lengths, determines reference wavelets, and quantifies surface quality through cross-correlation, providing a global quality score and enabling automatic, iterative optimization of surface selection using AI, ML, or dynamic programming algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to assess seismic surface quality, then human judgment can be applied, but human error and inconsistency increase
Solution Approach 1:
The patent replaces manual visual inspection and human judgment with an automated computer-implemented method that uses signal processing techniques (wavelet extraction, cross-correlation analysis) to objectively assess surface quality. This substitution eliminates human error and inconsistency while providing reproducible, quantitative quality scores.
Solution Approach 2:
The system enables surfaces to be automatically assessed without human intervention by using the seismic data itself to generate quality metrics. The method extracts wavelets from the seismic traces and uses cross-correlation to quantify surface quality, allowing the data to evaluate itself objectively.
2Reliability
If automated cross-correlation methods are used to quantify surface quality, then objectivity and consistency improve, but computational complexity increases
Solution Approach 1:
The patent divides the seismic trace into multiple wavelets of varying lengths around the surface, allowing the complex assessment to be broken down into manageable segments. Each wavelet is independently processed through cross-correlation, and results are aggregated to produce an overall quality score, making the complex task computationally tractable.
Solution Approach 2:
The method extracts wavelets of varying lengths (excessive action) to ensure comprehensive coverage of the surface feature, then uses cross-correlation to identify the optimal length. This approach ensures that sufficient data is processed to achieve reliable quality assessment while the system automatically determines the appropriate level of processing needed.
3Measurement precision
If wavelets of varying lengths are extracted and cross-correlated, then quality assessment accuracy improves, but processing time increases
Solution Approach 1:
The patent performs wavelet extraction and cross-correlation analysis in advance to establish baseline quality metrics before final surface interpretation. By pre-processing the seismic data and computing quality scores for multiple wavelet lengths, the system prepares results that can be quickly referenced during exploration decision-making, reducing overall processing time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for objective, automatic, and consistent quality assessment of seismic surfaces, reducing human error and improving the accuracy of subsurface interpretation, enabling better decision-making in drilling operations.
Implementation Method 1
quantifying a quality of the surface based on correlating the plurality of wavelets with each reference wavelet of the corresponding variable length
Data Source
AI summary
Some implementations of the present disclosure provide a method that include: accessing a set of seismic traces from a grid of locations inside an geo-exploration area, each seismic trace records seismic reflections from underneath the geo-exploration area at a location of the grid; accessing an input indicating a surface in the set of seismic traces; extracting a plurality of wavelets from the set of seismic traces, each wavelet covering an adjustable length around the surface; determining a reference wavelet for each wavelet of a corresponding adjustable length; and quantifying a quality of the surface based on correlating the plurality of wavelets with each reference wavelet of the corresponding adjustable length.