Seismic Wavelet Convolution for Thin Bedding Layer Resolution
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Solution Overview
Problem
Traditional seismic data processing is inadequate for resolving the thickness of thin bedding layers, which can contain hydrocarbon deposits, as it is limited by the one quarter wavelength threshold, preventing the discovery of certain oil and gas deposits.
Innovation Solution
The method involves receiving seismic data, identifying a source wavelet, generating a geological layer template with primary and secondary reflection interfaces, applying the source wavelet using a mathematical convolution operation to model seismic wave interference, and determining the location of the geological layer using a wavelet response template.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seismic data processing is used, then the processing method is simple, but the measurement precision is insufficient for thin bedding layers
Solution Approach 1:
The method segments the seismic signal processing into distinct components: extracting the source wavelet, identifying reflection events, calculating two-way travel time, and determining layer thickness. This segmentation allows each component to be processed with appropriate algorithms, improving overall measurement precision for thin layers while managing complexity through systematic breakdown of the processing workflow
Solution Approach 2:
The invention transitions from traditional amplitude-based seismic interpretation to a time-domain approach using wavelet convolution. By modeling the seismic response in the time domain and comparing it with actual seismic data, the method achieves superior thickness resolution for thin bedding layers, effectively adding a temporal dimension to the analysis that overcomes the quarter-wavelength limitation
2Measurement precision
If traditional seismic processing is used, then the processing time is short, but the measurement precision for thin layers is insufficient
Solution Approach 1:
The method performs preliminary extraction and characterization of the source wavelet before applying it to the seismic data. By pre-calculating the wavelet response and storing it for comparison, the method avoids repeated complex calculations during the actual thickness determination process, thereby improving measurement precision while managing processing time efficiently
Solution Approach 2:
The invention creates a synthetic seismic response by convolving the source wavelet with the geological layer template. This synthetic copy is then compared with the actual seismic data to identify thin bedding layers. The copying approach allows for rapid comparison and iteration, improving measurement precision without proportionally increasing 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 expands seismic interpretation techniques to resolve the position of thin geologic features, such as oil-rich stratigraphic sequences, beyond the resolution limits, allowing for the detection of hydrocarbon deposits in thin bedding layers.
Implementation Method 1
generating a wavelet response template by applying the source wavelet to the geological layer template using a mathematical convolution operation to model seismic wave interference caused by the primary and secondary reflection interfaces
Data Source
AI summary
Determining geological layer location in a subterranean formation, including receiving seismic data representing an interaction of the geological layer with propagation of a seismic wave, identifying a source wavelet representing a portion of the seismic wave impinging on a boundary of the geological layer, providing a geological layer template of the geological layer including primary and secondary reflection interfaces associated with reflectivity based on material properties of the geological layer, generating a wavelet response template by applying the source wavelet to the geological layer template using a mathematical convolution operation to model seismic wave interference caused by the primary and secondary reflection interfaces, identifying an extremum of the seismic data, and determining, based on the extremum, the location of the geological layer in the subterranean formation using the wavelet response template.