Seismic Source Separation via Monte Carlo Optimization
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Solution Overview
Problem
Seismic surveys face challenges in effectively separating energy signals from multiple interfering seismic sources, leading to residual and leakage energies that hinder accurate attribution of energy to specific sources, affecting the quality of hydrocarbon deposit detection in subterranean geological formations.
Innovation Solution
A computer-implemented system optimizes seismic survey parameters, including source geometry, receiver geometry, and firing timing, using a Monte Carlo simulation and numerical inversion algorithms to minimize residual and leakage energies, enabling precise separation of energy signals from multiple seismic sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple seismic sources are used to improve survey coverage and productivity, then the quantity of seismic data increases, but the ability to separate energy signals from specific sources deteriorates due to interfering energies
Solution Approach 1:
The system performs preliminary optimization of survey parameters (source positions, receiver positions, timing) using Monte Carlo simulations and numerical inversion algorithms before actual data collection. This preliminary action ensures that the survey design inherently minimizes interfering energies and maximizes source separability, allowing multiple sources to be used effectively without compromising separation accuracy.
Solution Approach 2:
The system optimizes specific survey parameters including source geometry, receiver geometry, and firing timing sequences. By carefully adjusting these parameters through numerical optimization, the system maintains high source separation accuracy even when using multiple seismic sources simultaneously or in rapid succession, thus resolving the contradiction between productivity and measurement precision.
2Measurement precision
If survey parameters are optimized for source separation, then measurement precision improves, but the complexity of survey design and execution increases
Solution Approach 1:
The system replaces complex manual survey design processes with automated computational methods. Monte Carlo simulations and numerical inversion algorithms automatically optimize survey parameters, eliminating the need for manual trial-and-error design approaches. This substitution of computational methods for mechanical/design complexity achieves high source separation accuracy while managing design complexity through automation.
Solution Approach 2:
The system uses Monte Carlo simulations to create virtual copies of the seismic survey scenario, allowing optimization to be performed in silico before actual deployment. These simulated models replicate the physical survey conditions and allow parameter optimization without affecting real-world survey complexity, enabling precise source separation through pre-computed optimal parameters.
3Measurement precision
If advanced numerical inversion algorithms are used to separate energy signals, then measurement precision improves, but loss of time in data processing increases
Solution Approach 1:
The system performs preliminary optimization of survey parameters using Monte Carlo simulations and numerical inversion algorithms before actual data collection. By pre-optimizing the survey design, the system minimizes the need for complex post-processing of actual field data, thereby reducing data processing time while maintaining high source separation accuracy.
Solution Approach 2:
The system applies numerical inversion algorithms selectively to optimize critical survey parameters that have the greatest impact on source separation, rather than processing all survey data through computationally intensive algorithms. This partial application of advanced processing achieves sufficient measurement precision while minimizing data processing time.
Data Source
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AI summary
A technique includes determining at least one parameter that characterizes a seismic survey in which multiple interfering seismic sources are fired and seismic sensors sense energy that is produced by the seismic sources. The determination of the parameter(s) includes optimizing the seismic survey for separation of the sensed energy according to the seismic sources.