Seismic Imaging Resolution via Data Subset Decomposition
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Solution Overview
Problem
Conventional seismic imaging techniques have limited resolution, failing to accurately depict fine subsurface features that may contain crucial information on stratigraphy and structure, which hinders the effective identification and production of hydrocarbon reservoirs.
Innovation Solution
A method involving the decomposition of seismic datasets into sub-sets based on specific functions, followed by imaging each subset using techniques like reverse time migration, and subsequent combination for improved spatial spectral resolution, resulting in higher resolution seismic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic imaging techniques are used, then the imaging process is simple and fast, but the resolution is limited and cannot accurately depict fine subsurface features
Solution Approach 1:
The seismic dataset is decomposed into multiple data subsets based on different frequency bands or wavefield components. Each subset is then imaged separately using migration techniques, and the resulting image subsets are combined to produce a high-resolution final image. This segmentation approach allows the system to achieve superior resolution by processing different frequency components independently rather than treating the entire dataset as a single unit.
2Measurement precision
If data decomposition and separate imaging of subsets is performed, then resolution is improved, but computational complexity and processing time increase
Solution Approach 1:
The seismic dataset is pre-decomposed into frequency-based or component-based subsets before the imaging process begins. This preliminary decomposition organizes the data in a way that enables parallel processing of different subsets through separate migration operations. By preparing the data structure in advance, the system can efficiently process multiple subsets simultaneously rather than sequentially, reducing overall processing time while maintaining high resolution.
3Loss of information
If conventional migration techniques are used, then processing is straightforward, but the ability to define location of rock and fluid property changes is insufficient
Solution Approach 1:
The imaging system applies different processing parameters and migration techniques to different data subsets based on their specific frequency characteristics and wavefield properties. Each subset is optimized for its particular geological features, allowing the system to capture local variations in rock and fluid properties with greater accuracy. This localized optimization ensures that no subsurface information is lost due to one-size-fits-all processing.
Data Source
AI summary
A method is described for seismic imaging including receiving a seismic dataset representative of a subsurface volume of interest and an earth model; decomposing the seismic dataset into a set of data sub-sets based on a decomposition function; imaging each data sub-set using the earth model to generate a set of image sub-sets; and combining the set of image sub-sets based on a criterion to create a high resolution seismic image. The method may be executed by a computer system.