Seismic Fault Interpretation via Bayesian Optimization
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Solution Overview
Problem
Conventional fault interpretation algorithms in seismic data analysis are time-consuming and prone to inaccuracies due to the complexity of seismic data and the need for extensive parameter tuning, which can be data-dependent and require numerous optimization trials.
Innovation Solution
The method involves optimizing fault interpretation algorithm parameters using a stochastic optimization technique like Bayesian Optimization, which updates interpretation parameters based on expert-labeled and algorithm-labeled seismic datasets, reducing the time and computational resources required for parameter tuning and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fault interpretation algorithms are used with extensive parameter tuning, then measurement precision of fault detection is improved, but loss of time increases significantly
Solution Approach 1:
The system performs preliminary action by pre-optimizing interpretation parameters using stochastic optimization techniques before actual fault detection. The optimization process evaluates multiple parameter sets in advance and selects the best configuration, so that when fault detection is performed, the parameters are already optimized, eliminating the need for time-consuming parameter tuning during actual operation.
Solution Approach 2:
The system implements self-service through automated stochastic optimization that self-adjusts interpretation parameters without human intervention. The optimization algorithm automatically evaluates parameter performance and selects optimal values based on seismic data characteristics, making the system self-tuning and eliminating manual parameter adjustment time.
2Measurement precision
If conventional fault interpretation algorithms with many operations are used, then measurement precision of fault detection is improved, but productivity decreases
Solution Approach 1:
The system applies parameter changes by dynamically adjusting interpretation parameters based on stochastic optimization results. Instead of using fixed or manually tuned parameters, the system automatically modifies parameters to optimize both accuracy and processing efficiency, resolving the contradiction between detailed multi-operation algorithms and processing speed.
3Measurement precision
If data-dependent parameter tuning is performed to improve fault detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system replaces manual mechanical parameter tuning with automated stochastic optimization. Instead of requiring experts to manually adjust parameters based on data characteristics, the system uses computational optimization algorithms to automatically determine optimal parameters, reducing operational complexity while maintaining or improving accuracy.
Data Source
AI summary
A method includes receiving a training selection of a first set of faults located in a first subset of a seismic dataset for a subsurface geologic formation, detecting a second set of faults in the seismic dataset based on fault interpretation operations using a first set of interpretation parameters, and determining a difference between the first set of faults and the second set of faults. The method also includes generating a second set of interpretation parameters for the fault interpretation operations based on the difference between the first set of faults and the second set of faults, and determining a feature of the subsurface geologic formation based on fault interpretation operations using the second set of interpretation parameters.