Seismic Data Processing Using Statistical Sampling and Iterative Reflection Model Updates
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Solution Overview
Problem
Current seismic data processing methods are inadequate for accurately identifying and isolating seismic signals from subsurface regions, particularly in multi-dimensional spaces, leading to inefficiencies in seismic imaging and hydrocarbon exploration.
Innovation Solution
A method that employs statistical sampling to select shot points and update reflection models based on an objective function representing the mismatch between sparse seismic data and simulated data, using an earth model and reflection model to iteratively refine the seismic data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If statistical sampling is used to select shot points, then data processing efficiency is improved, but measurement precision deteriorates
Solution Approach 1:
The patent implements an iterative feedback loop where the reflection model is continuously updated based on the objective function that measures the mismatch between sparse observed data and simulated data. This feedback mechanism allows the system to progressively improve measurement precision even when working with statistically sampled sparse data, resolving the contradiction between processing efficiency and measurement accuracy.
Solution Approach 2:
The patent changes the parameter representation by transforming the seismic imaging problem into a reflection model parameter estimation problem. By iteratively updating reflection model parameters based on sparse sampled data and comparing with simulated data, the system achieves high precision imaging while maintaining processing efficiency through parameter optimization rather than exhaustive data processing.
2Loss of time
If sparse seismic data is used, then processing time is reduced, but imaging resolution deteriorates
Solution Approach 1:
The patent creates simulated seismic data copies based on the reflection model and compares them with sparse observed data. This copying approach allows the system to iteratively refine the reflection model using minimal sparse data, achieving high-resolution imaging without requiring processing of complete dense datasets, thus reducing processing time while maintaining imaging quality.
Solution Approach 2:
By transforming the problem into reflection model parameter estimation and iteratively optimizing these parameters against sparse data, the patent achieves high-resolution imaging from compressed datasets. The parameter optimization process recovers fine details that would otherwise be lost in sparse sampling, resolving the contradiction between processing speed and imaging resolution.
3Measurement precision
If iterative reflection model updates are performed, then imaging accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and focuses computation only on updating the reflection model parameters rather than processing the entire seismic dataset iteratively. By isolating the essential parameter estimation problem and using sparse sampled data, the system reduces computational complexity while maintaining imaging accuracy through targeted iterative updates of only the necessary model components.
Data Source
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AI summary
Described 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 sparse seismic data by selecting shot points in the acquired seismic data using statistical sampling. The method may determine simulated seismic data based on an earth model for the region of interest, a reflection model for the region of interest, and the selected shot points. 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 reflection model using the objective function.