Seismic Imaging Task Clustering for Precise Subsurface Interpretation
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Solution Overview
Problem
Existing seismic imaging technologies face challenges in efficiently processing and interpreting seismic data to accurately characterize subsurface formations, particularly in complex environments with noise interference and varying geological structures, which affects the precision of reservoir characterization and drilling operations.
Innovation Solution
A method and system for seismic imaging that involves defining input domains, clustering samples and tasks, and ordering clusters based on a sharing network to enhance the processing and interpretation of seismic data, improving the understanding of subsurface structures and facilitating more accurate control of drilling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seismic data processing methods are used, then processing speed is maintained, but measurement precision and characterization accuracy deteriorate due to noise interference and complex geological structures
Solution Approach 1:
The patent segments the seismic imaging workflow into multiple discrete tasks (e.g., noise attenuation, migration, inversion) that can be independently clustered and processed. Each task operates on specific input domains and produces targeted outputs, allowing complex processing to be broken down into manageable, precision-optimized segments rather than applying a single complex processing chain to all data.
Solution Approach 2:
The patent changes the organizational parameters of seismic processing by introducing task clustering based on input domain similarities and sharing networks. Tasks are grouped according to their data requirements and computational characteristics, transforming the processing architecture from a linear sequence to a clustered, optimized execution model that improves precision while managing complexity through systematic reorganization.
2Measurement precision
If comprehensive seismic data processing is performed to improve subsurface characterization, then processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary clustering of tasks and identification of input domain sharing relationships before actual seismic processing begins. By pre-organizing the computational workflow and identifying which tasks share common input domains, the system prepares an optimized execution plan that reduces redundant computations and data loading during the actual processing phase, thereby reducing overall processing time while maintaining comprehensive analysis.
Solution Approach 2:
The patent merges tasks that share common input domains into clustered groups that can be executed efficiently together. By combining tasks with overlapping data requirements into unified processing clusters, the system eliminates redundant data loading and processing operations, reducing total computation time while still performing comprehensive seismic characterization through the combined output of multiple specialized tasks.
3Productivity
If multiple independent tasks are processed separately, then task modularity is maintained, but processing efficiency deteriorates due to redundant data loading and computation
Solution Approach 1:
The patent introduces an intermediary clustering layer that sits between individual tasks and the data storage system. This clustering mechanism acts as a mediator that identifies shared input domains across multiple tasks and coordinates data loading at the cluster level rather than individually for each task. The intermediary structure manages the complexity of task coordination by providing a systematic framework for identifying and exploiting sharing relationships, thereby improving efficiency without overwhelming complexity.
Solution Approach 2:
The patent creates universal input domain clusters that can serve multiple tasks simultaneously. Rather than dedicating separate data processing paths for each task, the system establishes universal clusters of input data that can be consumed by multiple downstream tasks within their respective clusters. This multi-functional approach allows the same processed data to benefit multiple analytical tasks, improving overall productivity while the clustering framework manages the coordination complexity.
Data Source
AI summary
A method can include defining an input domain for each of multiple tasks of a seismic imaging workflow to define multiple input domains for seismic data; generating multiple samples from the input domain for each of the multiple tasks; clustering the multiple samples from the multiple input domains to generate input domain clusters; assigning a number of the input domain clusters to each of the multiple tasks, where one or more of the input domain clusters are shared by more than one of the multiple tasks; clustering the multiple tasks, based on a sharing network of the input domain clusters, to generate clusters of the multiple tasks; and ordering the clusters of the multiple tasks to generate an order.