Seismic Imaging Framework for Acquisition Footprint Reduction
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Solution Overview
Problem
Existing seismic data processing methods struggle with acquisition footprint artifacts, leading to inaccurate subsurface formation analysis and poor data quality, particularly in complex geological environments like subsalt regions.
Innovation Solution
A method and system for automatically identifying and reducing acquisition footprint artifacts in seismic data by extracting reduction parameters based on the type of artifact, using advanced processing techniques and frameworks like PETREL and DELFI, which enhance data quality and improve subsurface imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seismic data processing methods are used, then processing speed and simplicity are maintained, but data quality and accuracy of subsurface formation analysis deteriorate due to acquisition footprint artifacts
Solution Approach 1:
The processing system segments the seismic data into multiple subsets based on acquisition footprint characteristics, applying different processing parameters to each subset. This allows targeted reduction of artifacts while maintaining overall data quality without requiring complete reprocessing of all data with uniform complex algorithms.
Solution Approach 2:
The system performs preliminary identification and classification of acquisition footprint artifacts before main processing. By detecting artifact types and characteristics in advance, the system can pre-configurate appropriate processing parameters and filters, reducing the complexity of subsequent processing steps while improving overall data quality.
2Measurement precision
If advanced artifact reduction techniques are applied, then subsurface imaging accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies artifact reduction processing selectively to only those data subsets that contain significant acquisition footprint artifacts, rather than processing all seismic data uniformly. This partial action approach maintains high subsurface imaging accuracy for affected regions while reducing overall processing time and computational resource consumption.
Solution Approach 2:
The processing system dynamically adjusts processing parameters based on the detected type and severity of acquisition footprint artifacts. By changing parameters such as filter strength, frequency bands, and processing intensity according to actual artifact characteristics, the system achieves high imaging accuracy without applying excessive processing uniformly, thus optimizing processing time.
3Productivity
If manual artifact identification and processing parameter selection are used, then processing flexibility is maintained, but productivity and automation level decrease
Solution Approach 1:
The processing system automatically identifies acquisition footprint artifact types, characterizes their properties, and selects appropriate processing parameters without requiring manual intervention. The system serves itself by autonomously completing the entire workflow from artifact detection to parameter selection and application, significantly improving productivity while maintaining high automation level.
Solution Approach 2:
The system implements feedback loops where processing results are continuously evaluated and used to adjust subsequent processing steps. Automated monitoring of data quality metrics provides feedback that guides further artifact reduction operations, enabling high productivity through systematic automation while maintaining processing flexibility through adaptive parameter adjustment based on observed results.
Data Source
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AI summary
A method can include accessing data that include seismic data of a subsurface region; automatically identifying a type of acquisition footprint artefact to reduce based at least in part on at least a portion of the data; automatically extracting acquisition footprint artefact reduction parameters based at least in part on the type of acquisition footprint artefact; and reducing the acquisition footprint artefact using the acquisition footprint artefact reduction parameters.