Seismic Data Processing Using Statistical Sampling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current seismic data processing methods are inefficient in accurately identifying and isolating seismic signals from subsurface regions, leading to computational challenges and potential aliasing issues due to inadequate sampling techniques.
Innovation Solution
The method employs statistical sampling based on exclusion criteria and earth models to determine sparse seismic data, simulates seismic data using an earth model, and updates the earth model using an objective function that represents the mismatch between observed and simulated data, facilitating hydrocarbon exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional seismic data processing methods are used to identify and isolate seismic signals, then comprehensive signal coverage is achieved, but computational efficiency decreases and aliasing issues occur due to inadequate sampling
Solution Approach 1:
The method performs preliminary statistical sampling of shot points using exclusion criteria before full processing. By pre-selecting a sparse subset of shot points that satisfy the exclusion criterion (minimum distance separation), the system reduces computational workload while maintaining signal identification accuracy. This preliminary action prevents aliasing by ensuring adequate sampling density in the selected subset.
Solution Approach 2:
The invention changes the sampling parameter from traditional uniform or dense sampling to statistical sampling with exclusion criteria. By transforming the sampling strategy and introducing the exclusion radius parameter, the system achieves both computational efficiency and signal accuracy. The exclusion criterion parameter controls the minimum distance between selected shot points, optimizing the balance between data volume and signal quality.
2Measurement precision
If dense sampling of shot points is performed to ensure adequate coverage, then signal identification accuracy improves, but computational costs increase
Solution Approach 1:
Instead of processing all shot points (excessive action), the method selectively processes a sparse subset of shot points that satisfies the exclusion criterion. This partial action is sufficient to achieve accurate signal identification while dramatically reducing computational cost. The exclusion criterion ensures that the selected subset maintains adequate spatial distribution for accurate subsurface imaging.
Solution Approach 2:
The method extracts a representative sparse subset of shot points from the complete dataset by applying the exclusion criterion. This extraction process removes redundant or overly dense shot points while retaining those that provide essential information for signal identification. The result is a reduced dataset that maintains measurement precision with lower computational requirements.
3Productivity
If statistical sampling with exclusion criterion is used to reduce data volume, then computational efficiency improves, but data coverage may be insufficient
Solution Approach 1:
The invention transforms the sampling approach by introducing the exclusion criterion parameter, which ensures that selected shot points maintain adequate spatial distribution. This parameter change prevents information loss by guaranteeing that the sparse subset captures essential subsurface variations. The exclusion radius is calibrated to maintain sufficient coverage while enabling faster processing.
Solution Approach 2:
The method employs an iterative feedback mechanism where the earth model is updated by minimizing the mismatch between sparse observed seismic data and simulated data. This feedback loop ensures that the sparse subset of shot points provides sufficient information for accurate model updating. The objective function guides the selection and processing to maintain data coverage adequacy despite reduced data volume.
Data Source
AI summary
Disclosed herein are implementations of various technologies for a method for seismic data processing. The method may receive seismic data for a region of interest. The seismic data may be acquired in a seismic survey. The method may determine an exclusion criterion. The exclusion criterion may provide rules for selecting shot points in the acquired seismic data. The method may determine sparse seismic data using statistical sampling based on the exclusion criterion and the acquired seismic data. The method may determine simulated seismic data based on the earth model and shot points corresponding to the sparse seismic data. The method may determine an objective function that represents a mismatch between the sparse seismic data and the simulated seismic data. The method may update the earth model using the objective function.