Seismic Texture Attributes for Hydrocarbon Region Ranking
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Solution Overview
Problem
Current seismic data analysis methods fail to effectively distinguish and prioritize regions based on their seismic texture for hydrocarbon exploration, lacking an automated and efficient means to identify and rank potential hydrocarbon-bearing areas.
Innovation Solution
The method involves transforming seismic survey data into a multidimensional spectral attribute data volume by selecting a 2D or 3D analysis window, transforming data into the wavenumber domain, and defining spectral attributes, which are then computed and assigned to spatial locations, using transforms like Fourier, Bessel, and Hankel transforms to create attributes sensitive to subsurface geophysical features indicative of hydrocarbon potential.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seismic data analysis methods are used, then the analysis process is simple, but the ability to distinguish and prioritize regions based on seismic texture is insufficient
Solution Approach 1:
The patent transforms seismic data from traditional time-space domain analysis to wavenumber-frequency domain analysis using Fourier transforms. This dimensional transformation enables extraction of spectral texture attributes that are not visible in conventional displays, improving the ability to distinguish hydrocarbon-bearing regions while maintaining manageable complexity through automated processing.
Solution Approach 2:
The patent introduces new spectral parameters (wavenumber, frequency spectrum, texture attributes) to characterize seismic data differently from traditional amplitude and time-based parameters. By computing spectral density, coherence, and other frequency-domain attributes, the method enhances texture discrimination capability without requiring complex hardware modifications.
2Productivity
If manual seismic interpretation is used, then the interpretation process is thorough, but the productivity and efficiency are low
Solution Approach 1:
The patent implements automated classification algorithms that self-adjust and prioritize seismic regions based on computed spectral texture attributes. The system automatically identifies and ranks hydrocarbon-potential regions without requiring extensive manual interpretation time, achieving both high productivity and reasonable processing time through algorithmic self-optimization.
Solution Approach 2:
The patent uses powerful computational algorithms and advanced spectral analysis methods to rapidly process large volumes of seismic data. The accelerated processing capability enables thorough analysis of entire seismic volumes in reduced time, dramatically improving exploration productivity while maintaining interpretation quality.
3Reliability
If comprehensive seismic attribute analysis is performed, then the identification of hydrocarbon features is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent divides the seismic data volume into smaller analysis windows or blocks, computing spectral attributes for each segment independently. This segmentation approach maintains high identification reliability by analyzing local textures in detail, while reducing computational complexity by processing manageable chunks rather than the entire volume at once.
Solution Approach 2:
The patent extracts only the most relevant spectral texture attributes from the full seismic dataset for classification and interpretation. By selecting and computing only the critical frequency-domain parameters needed for hydrocarbon identification, the method maintains high reliability while avoiding the computational burden of analyzing all possible attributes.
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 enables automated partitioning and ranking of seismic data regions based on their hydrocarbon potential, enhancing the efficiency and accuracy of hydrocarbon exploration by highlighting regions with higher hydrocarbon significance.
Implementation Method 1
transforming the data within the window to a spectrum in a wavenumber domain
Data Source
AI summary
Method for generating a new family of seismic attributes sensitive to seismic texture that can be used for classification and grouping of seismic data into seismically similar regions. A 2D or 3D data analysis window size is selected (23), and for each of multiple positions (25) of the analysis window in the seismic data volume, the data within the window are transformed to a wavenumber domain spectrum (26). At least one attribute of the seismic data is then defined based on one or more spectral properties, and the attribute is computed (28) for each window, generating a multidimensional spectral attribute data volume (29). The attribute data volume can be used for inferring hydrocarbon potential, preferably after classifying the data volume cells based on the computed attribute, partitioning the cells into regions based on the classification, and prioritizing of the regions within a classification.