Dip-Guided Seismic Image Stacking for Noise Attenuation
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Solution Overview
Problem
Seismic imaging in complex subsurface areas, such as near salt bodies, often results in contaminated images due to steeply dipping interfaces that do not reflect seismic energy effectively, leading to poor interpretation of hydrocarbon reservoirs.
Innovation Solution
A method involving selective stacking of unstacked or partially stacked seismic images using a dip dataset to compute weighting functions based on slant stacking, which are then applied to enhance amplitude-processed images, thereby creating a stacked seismic image that attenuates noise and enhances relevant geological features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional stacking methods are used to process seismic data, then processing speed is maintained, but image clarity and accuracy deteriorate due to noise contamination from steeply dipping interfaces
Solution Approach 1:
The patent segments the stacking process by introducing dip-based weighting functions that selectively weight different seismic events based on their dip characteristics. This segmentation allows the processing system to differentiate between useful reflections and harmful steeply dipping events, improving image clarity without requiring complete reprocessing of all data
Solution Approach 2:
The patent applies local quality by making the stacking process adaptive to local dip conditions. The weighting function W(x,ω,η) varies spatially and spectrally to suppress steeply dipping events in specific locations while preserving useful reflections, thereby improving local image quality without uniform processing across the entire dataset
2Reliability
If dip-guided selective stacking is applied to improve image quality, then noise attenuation improves, but processing complexity increases
Solution Approach 1:
The patent introduces dip-weighting functions as an intermediary mechanism between the raw seismic data and the final stacked image. These weighting functions act as a mediator that selectively attenuates noise based on dip characteristics without requiring complex iterative processing or advanced imaging algorithms, thus improving reliability while controlling complexity
Solution Approach 2:
The patent changes the processing parameters by incorporating dip information and spectral weighting into the stacking process. By modifying the weighting function parameters W(x,ω,η) based on dip angle and frequency content, the system achieves better noise attenuation through parameter optimization rather than structural complexity
3Measurement precision
If amplitude processing is performed on each single-shot migrated image, then image quality improves, but processing time increases
Solution Approach 1:
The patent applies partial action by performing amplitude processing selectively on images that require it, rather than uniformly processing all single-shot migrated images. The dip-weighted stacking identifies which images benefit from amplitude processing and applies it only to those cases, improving amplitude accuracy while reducing overall processing time
Data Source
AI summary
A method is described for seismic imaging of the subsurface using dip-guided optimized stacking. The method computes weighting functions for a plurality of single-shot migrated images, unstacked seismic images, or partially stacked seismic images based on a slant stack performed using an input dip dataset; applying the plurality of weighting functions to the plurality of single-shot migrated images, unstacked seismic images, or partially stacked seismic images, or a plurality of dip-filtered images to create a plurality of weighted images; and summing the plurality of weighted images into a stacked seismic image. The method may be executed by a computer system.