Guided Geoscience Workflows for Cross-Domain Data Processing
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Solution Overview
Problem
Existing data processing systems for geoscience face challenges in handling diverse and complex datasets due to limited labeled data, variability in workflows, and lack of explicit feedback, making it difficult to train effective models that can adapt to different conditions and geological zones.
Innovation Solution
A hierarchical data processing system using global and local models to unify data sources across domains, incorporating user and group-specific models to adapt workflows dynamically, and integrate multiple software modules for tailored processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-specific processing workflows are used, then processing accuracy for specific data types is improved, but adaptability across different domains deteriorates
Solution Approach 1:
The system segments processing workflows into domain-specific modules (seismic data processing, well log processing, core data processing) that can be independently selected and combined. This allows the system to maintain specialized accuracy for each domain while providing flexibility to switch between domains as needed.
Solution Approach 2:
The system creates a universal workflow platform that can handle multiple data types through a common architecture. The unified workflow manager and consistent data structures enable the same system to process seismic data, well logs, and core data, achieving cross-domain adaptability while maintaining domain-specific processing capabilities through configurable modules.
2Measurement precision
If labeled data is used for training models, then model accuracy is improved, but data collection effort and time deteriorate
Solution Approach 1:
The system automatically generates training data by executing workflows and capturing outcomes without requiring manual labeling. The workflow execution itself serves as the labeling process, where the system learns from actual processing results and user feedback automatically, eliminating the time-consuming manual annotation process.
Solution Approach 2:
The system incorporates feedback loops where processing outcomes and user evaluations are automatically fed back into the training process. This continuous feedback mechanism enables the system to improve model accuracy using real-world performance data rather than requiring extensive pre-labeled datasets, significantly reducing data collection time.
3Adaptability or versatility
If general models are used, then adaptability across domains is improved, but processing specificity and expertise deteriorate
Solution Approach 1:
The system divides the general model into domain-specific sub-models or processing modules that can be selectively activated. Each module maintains specialized knowledge for its specific domain (seismic, well log, core) while the overall system architecture remains unified and adaptable, allowing the system to switch between specialized modes as needed.
Data Source
AI summary
Data processing systems and processing methods are configured to process geoscience data for a set of different data domains. The data processing systems described herein are configured to unify data sources from the set of different domains to process data in the context of each of the different domains of the set. The data processing system can use data, such as training data or trained model weightings, that are generated when performing a processing workflow in a first domain for updating how a second processing workflow is performed for a second, different domain, and vice versa. The data processing system can improve both processing workflows based on the contexts of the other workflow being performed.