Seismic Processing Recommendation Engine
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Solution Overview
Problem
Traditional seismic data processing methods are time-consuming, iterative, and prone to human error due to the need for manual parameter adjustments and domain expertise, making them computationally expensive and inefficient.
Innovation Solution
An automated seismic data processing workflow using a recommendation engine with machine learning models that predict optimal processing sequences and parameter sets, leveraging historical data to streamline the processing of seismic data and reduce human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual parameter adjustments and domain expertise are used in traditional seismic data processing, then processing accuracy can be maintained through human judgment, but processing time increases and human error becomes more likely
Solution Approach 1:
The system enables self-service by allowing the seismic processing system to automatically select and apply appropriate geophysical algorithms and parameters without requiring manual intervention from geophysicists. The automated workflow engine evaluates seismic data characteristics and autonomously determines processing parameters, eliminating the time-consuming manual adjustment process while maintaining consistent accuracy standards.
Solution Approach 2:
The patent replaces the mechanical system of manual human judgment and interaction with an automated computational system. Machine learning models and automated workflow engines substitute for human geophysicists in selecting parameters and algorithms, transforming the processing workflow from a manual, iterative process to an automated, efficient system that reduces both time and human error.
2Reliability
If rigorous human interaction is required for seismic data processing, then parameter validation can be performed, but the workflow becomes highly iterative and computationally expensive
Solution Approach 1:
The system implements feedback mechanisms where the automated workflow engine continuously evaluates processing results and adjusts parameters based on performance metrics. Machine learning models learn from historical processing outcomes and provide feedback to optimize future parameter selections, enabling automated validation that is both rapid and accurate, eliminating the need for multiple manual iteration cycles.
Solution Approach 2:
The patent applies preliminary action by pre-configuring and pre-validating geophysical algorithms and parameter sets before they are applied to seismic data. The system maintains a library of pre-tested processing workflows that have been validated in advance, allowing the automated engine to select and execute proven processing sequences without requiring real-time human validation, thus improving workflow efficiency while maintaining reliability.
3Manufacturing precision
If multiple iterations are performed to find optimal parameters, then processing quality can be improved, but computational cost and processing time increase significantly
Solution Approach 1:
The system performs preliminary action by pre-processing and pre-evaluating multiple parameter combinations offline before actual seismic data processing. The automated workflow engine prepares optimized parameter sets in advance based on seismic data characteristics, so that during actual processing, the system can directly apply pre-validated parameters without requiring multiple costly iterative computations, thus maintaining high processing quality while reducing computational cost.
Solution Approach 2:
The patent uses copying by creating and storing templates of successful processing workflows and parameter sets that can be reused across similar seismic datasets. Instead of performing multiple iterations for each new dataset, the system copies and adapts proven processing sequences from historical data, significantly reducing computational cost while maintaining consistent processing quality across different projects.
4Reliability
If conventional seismic data processing workflows are used, then domain expertise can be applied, but the workflow requires rigorous human interaction making it time-consuming
Solution Approach 1:
The system enables self-service by embedding domain expertise within the automated workflow engine through machine learning models trained on historical processing data. The engine autonomously evaluates seismic data characteristics and selects appropriate geophysical algorithms and parameters without requiring human geophysicists to manually apply their expertise, thus maintaining the benefits of domain knowledge while dramatically simplifying the operational workflow and reducing time requirements.
Data Source
AI summary
System and methods for automated seismic processing are provided. Historical seismic project data associated with one or more historical seismic projects is obtained from a data store. The historical seismic project data is transformed into seismic workflow model data. At least one seismic workflow model is generated using the seismic workflow model data. Responsive to receiving seismic data for a new seismic project, an optimized workflow for processing the received seismic data is determined based on the at least one generated seismic workflow model. Geophysical parameters for processing the seismic data with the optimized workflow are selected. The seismic data for the new seismic project is processed using the optimized workflow and the selected geophysical parameters.