Wavelet-Split Seismic Attribute for Subsurface Feature Identification
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Solution Overview
Problem
Current seismic data processing methods struggle to effectively identify and interpret subsurface features beyond basic seismic amplitude, such as faults, stratigraphic unconformities, and variations in layer thickness, which are crucial for hydrocarbon accumulation prospecting.
Innovation Solution
The method involves computing wavelet-split attributes from seismic data, which indicate locations where seismic wavelets split, and using these attributes to generate connected strings or loops that represent geobodies, allowing for the interpretation of subsurface structure and stratigraphy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic seismic amplitude is used for subsurface interpretation, then the interpretation process is simple, but subsurface features such as faults, stratigraphic unconformities, and layer thickness variations cannot be effectively identified
Solution Approach 1:
The patent segments seismic wavelets into multiple frequency components through spectral decomposition. By analyzing different frequency bands separately, the method identifies subsurface features that are not visible in the basic amplitude data, thereby improving measurement precision without requiring entirely new processing approaches
Solution Approach 2:
The patent transforms the interpretation from a single amplitude dimension to multiple dimensions by introducing frequency as an additional dimension. This allows features like faults and unconformities to be identified through their frequency-dependent characteristics, improving detection accuracy while adding manageable complexity
2Measurement precision
If multiple seismic attributes are computed to highlight specific features, then feature detection capability improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent creates a multi-functional processing framework where spectral decomposition serves multiple purposes simultaneously: it enhances feature detection, enables frequency-based classification of wavelet splits, and provides a unified approach for identifying different subsurface features. This reduces the need for separate processing workflows for different features
Solution Approach 2:
The patent performs spectral decomposition and identifies wavelet split locations before detailed feature classification. By pre-processing the data to highlight potential feature locations through frequency analysis, the method reduces the computational burden of subsequent detailed interpretation and improves overall processing efficiency
3Extent of automation
If phase residues are used to identify wavelet split locations, then automatic feature recognition improves, but processing time and computational resources increase
Solution Approach 1:
The patent replaces manual interpretation methods with automated computational algorithms for identifying wavelet splits. By using phase residue calculations and spectral decomposition algorithms, the system automatically detects features without requiring manual picking, significantly improving automation extent while the efficient algorithms keep processing time manageable
Data Source
AI summary
A new seismic attribute is disclosed along with its use to locate and classify seismic waveform anomalies and use them to construct objects and from them geologic surfaces (77) and bodies (75) from which hydrocarbon potential (or quality control of the seismic acquisition and processing (71)) may be assessed (79). The seismic attribute is constructed (74) from determinations of phase residues in the seismic data volume (72), preferably using complex trace analysis but alternatively by comparing neighboring waveforms for disappearing waveshape inflections. It is shown that in a data volume of the new attribute, non-zero values form strings and loops that may be associated with objects (geobodies) or surfaces such as unconformities or flooding surfaces. Methods of classification and selection (76) to reduce the number of objects generated are provided.