ML Model Deployment Metadata for Scalable Subsurface Interpretation
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Solution Overview
Problem
Current oil and gas exploration methods face challenges in accurately interpreting and modeling subsurface geologic environments, leading to inefficiencies in resource extraction and reservoir characterization.
Innovation Solution
A system incorporating a machine learning model training framework, metadata configurator, and deployment manager to generate and deploy trained machine learning models for improved subsurface interpretation and resource extraction, utilizing frameworks like DRILLPLAN, PETREL, TECHLOG, PETROMOD, ECLIPSE, and INTERSECT for enhanced data analysis and simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis and modeling methods are used for subsurface interpretation, then the process is simpler and more straightforward, but the accuracy of subsurface modeling and resource extraction is reduced
Solution Approach 1:
The patent segments the machine learning model deployment into distinct modular components: model training framework, metadata configurator, deployment manager, and monitoring system. Each component handles specific tasks independently, allowing the complex ML system to be managed through standardized interfaces and configurations, thereby achieving high accuracy without overwhelming system complexity
Solution Approach 2:
The patent introduces metadata as an intermediary layer between the machine learning models and the subsurface interpretation processes. This metadata standardizes model configurations, data formats, and operational parameters, enabling accurate ML-based subsurface modeling while maintaining system manageability through standardized communication protocols
2Productivity
If machine learning models are deployed to multiple remote devices simultaneously, then the productivity and coverage of resource extraction operations are improved, but the complexity of model management and updates increases
Solution Approach 1:
The deployment manager is designed as a universal system that can deploy, manage, and update machine learning models across multiple diverse remote devices simultaneously. It provides standardized deployment interfaces and centralized control capabilities, enabling the same model management infrastructure to serve various devices with different capabilities and requirements, thereby improving productivity without proportionally increasing management complexity
Solution Approach 2:
The system performs preliminary model training, validation, and metadata configuration centrally before deployment to remote devices. This advance preparation ensures models are optimized and ready for deployment, reducing the complexity of on-device management and enabling efficient simultaneous deployment across multiple devices with standardized pre-configured models
3Measurement precision
If traditional interpretation methods are used, then the system is easier to operate and maintain, but the accuracy of drilling operations and reservoir characterization is reduced
Solution Approach 1:
The monitoring system automatically tracks model performance, detects drift or degradation, and triggers retraining workflows without manual intervention. This self-service capability maintains high drilling operation accuracy by ensuring models remain optimized, while reducing operational complexity through automated rather than manual model lifecycle management
Solution Approach 2:
The system implements continuous feedback loops where model performance is monitored, evaluated, and used to trigger automated retraining when accuracy thresholds are not met. This feedback mechanism ensures high accuracy in drilling operations by continuously optimizing models, while the automated nature of the feedback process simplifies operation compared to manual model management
Data Source
AI summary
A system can include a machine learning model training framework that generates trained machine learning models; a metadata configurer that generates metadata for trained machine learning model implementation; and a deployment manager that deploys trained machine learning models, metadata or trained machine learning models and metadata to remote devices according to one or more implementation strategies.