Seismic Imaging Framework with Gradient Conditioning
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Solution Overview
Problem
Existing seismic data processing methods struggle with accurately modeling subsurface structures due to issues like near-surface heterogeneity, coverage gaps, and illumination deficiencies, leading to false anomalies and incomplete subsurface imaging.
Innovation Solution
A method and system are developed to generate synthetic seismic data using a wavelet and velocity model, apply a gradient conditioner, and perform iterative inversion to enhance the accuracy of subsurface modeling, incorporating features like seismic attributes and machine learning for improved data interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional seismic data processing methods are used, then the processing workflow is simpler, but the imaging resolution is lower and false anomalies increase due to near-surface heterogeneity and illumination deficiencies
Solution Approach 1:
The processing workflow is segmented into distinct modules: gradient conditioner generation, iterative inversion process, and final imaging. This allows complex processing to be broken down into manageable steps that can be applied systematically to improve imaging resolution while maintaining workflow organization
Solution Approach 2:
A gradient conditioner is generated in advance before the main inversion process. This preliminary action prepares the data by pre-processing gradients to account for near-surface heterogeneity and illumination variations, thereby improving final imaging resolution without adding complexity during the main processing stage
2Reliability
If conventional inversion processes are used, then the computational cost is lower, but coverage gaps and illumination deficiencies lead to incomplete subsurface imaging
Solution Approach 1:
The iterative inversion process uses feedback loops where synthetic seismic data is continuously compared with actual seismic data, and the velocity model is updated based on the misfit. This feedback mechanism ensures that coverage gaps and illumination deficiencies are progressively addressed, improving imaging completeness while managing computational resources efficiently
Solution Approach 2:
The inversion process iteratively changes velocity model parameters to minimize the difference between synthetic and observed seismic data. By systematically adjusting these parameters across multiple iterations, the method achieves more complete subsurface imaging without requiring excessive computational resources
3Manufacturing precision
If velocity models without gradient conditioning are used, then the processing is faster, but near-surface heterogeneity causes false anomalies
Solution Approach 1:
The gradient conditioner is generated in advance to pre-process the velocity model data, accounting for near-surface heterogeneity effects. This preliminary conditioning prevents false anomalies from developing during the main inversion process, improving velocity model accuracy without requiring excessive processing time during the critical inversion stage
Data Source
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AI summary
A method can include performing a simulation to generate synthetic seismic data using a wavelet and a velocity model of a subsurface geologic region, where the velocity model includes representations of subsurface structural features and associated geophysical parameters; generating a velocity gradient conditioner for the velocity model based at least in part on the synthetic seismic data; performing an iterative inversion process using acquired seismic data for the subsurface geologic region, the velocity model, and the velocity gradient conditioner; and outputting a final velocity model of the subsurface geologic region as a result of the iterative inversion process.