Sparse OBN Survey Layout Using FWI Kernel Analysis for Subsalt Imaging
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Solution Overview
Problem
Seismic data acquisition in complex geological areas, such as those with salt bodies, often fails to provide detailed subsurface velocity models due to challenges in imaging below complex geological bodies and at depth, limiting the accuracy of hydrocarbon deposit detection.
Innovation Solution
Employing sparse ocean bottom node (OBN) surveys combined with full waveform inversion (FWI) sensitivity kernel analysis to design optimal OBN geometries, including node and source patterns, to enhance seismic data acquisition and improve subsurface imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If towed streamer geometries are used for seismic data acquisition, then velocity model accuracy is improved, but imaging detail below complex geological bodies and at depth deteriorates
Solution Approach 1:
The patent segments the seismic data acquisition system into two distinct components: towed streamers for acquiring broad coverage velocity model data, and sparse OBN (ocean bottom nodes) for acquiring detailed imaging data below complex geological bodies. This segmentation allows each component to optimize for its specific function, with streamers providing accurate velocity models and OBN providing detailed subsalt imaging information that neither system can achieve alone.
2Measurement precision
If dense OBN surveys are deployed to improve subsurface imaging, then imaging quality is improved, but acquisition cost and complexity increase
Solution Approach 1:
The patent applies local quality by deploying OBN sensors selectively in specific locations where complex geological structures (such as salt bodies) are present, rather than uniformly across the entire survey area. The density and distribution of OBN nodes are optimized locally to capture the necessary diving wave information for FWI, reducing overall deployment complexity while maintaining imaging quality where it is most needed.
Solution Approach 2:
The patent uses partial action by implementing sparse OBN coverage rather than dense uniform coverage. The OBN nodes are strategically positioned to capture essential diving wave energy for FWI analysis, providing sufficient imaging detail below complex geological bodies without the excessive cost and complexity of a fully dense OBN survey. The sparse geometry is validated using FWI sensitivity kernel analysis to ensure adequate sampling.
3Productivity
If sparse OBN geometries are used to reduce acquisition cost, then acquisition efficiency is improved, but velocity model detail and subsurface imaging deteriorates
Solution Approach 1:
The patent performs preliminary action by conducting FWI sensitivity kernel analysis during the survey design phase to predict and validate that a sparse OBN geometry will provide sufficient sampling for accurate velocity modeling. This preliminary validation ensures that the reduced-density OBN deployment will not compromise the quality of velocity models or subsurface imaging, allowing cost-effective sparse geometries to be confidently implemented.
Data Source
AI summary
A method may include ocean bottom sensor (OBS) acquisition designed for complex earth model building and imaging with full waveform inversion (FWI) technology. Various combinations of OBS source and receiver patterns, as well as source design may be validated with FWI sensitivity kernel analysis technique. Usually, OBS design is done using conventional methods based on wavelength, frequency requirements, ray tracing and rules of thumps. The method of the disclosed embodiments utilizes OBS design and acquisition combined with FWI sensitivity kernel analysis.