Interpretable ML Platform for Oil and Gas Sweet Spot Prediction
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Solution Overview
Problem
In oil and gas exploration, existing methods for determining sweet spots rely on limited data sources and traditional physics models, failing to comprehensively utilize all available data for accurate predictions.
Innovation Solution
A machine learning platform that integrates and interpolates data from various sources to generate interpretable models, utilizing Generalized Additive Models with shape constraints to predict outcomes by relating predictive variables to response variables, supporting geologists and engineers in drilling decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional physics models are used for sweet spot identification, then the method is simple and interpretable, but the prediction accuracy is poor due to limited data utilization
Solution Approach 1:
The model segments the complex prediction task into multiple additive components, each representing the effect of a specific predictive variable. This allows the model to process multiple data sources separately and then combine them, improving data utilization while maintaining interpretability through the additive structure.
Solution Approach 2:
The patent transforms the modeling approach by changing from traditional physics-based parameters to data-driven parameters derived from multiple sources including well logs, seismic data, and production data. This parameter transformation enables comprehensive data utilization while the shape constraints maintain physical interpretability.
2Measurement precision
If comprehensive data from multiple sources is integrated, then the prediction accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent merges multiple disparate data sources (well logs, seismic data, production data) into a unified predictive framework. The additive model structure naturally combines these different data types, allowing comprehensive data utilization while the modular structure keeps processing manageable.
Solution Approach 2:
The patent introduces shape constraints as an intermediary mechanism that mediates between raw data from multiple sources and the final prediction. These constraints act as a bridge that integrates diverse data while maintaining physical interpretability and reducing processing complexity through standardized constraint types.
3Ease of operation
If interpretable models are used, then the model can be evaluated and altered by experts, but the model flexibility is limited compared to black-box models
Solution Approach 1:
The patent makes the model dynamic by allowing shape constraints to be selected and adjusted based on domain knowledge. Experts can choose different constraint types (monotonic, convex, concave) for different variables, and the model adapts to different data configurations while maintaining interpretability through the additive structure.
Solution Approach 2:
The patent enables model flexibility through parameter changes in the shape constraints. By adjusting the type and strength of constraints on different predictive variables, the model can adapt to various scenarios and expert preferences while remaining interpretable and evaluable by domain experts.
Data Source
AI summary
A system, method and program product for implementing a machine learning platform that processes a data map having feature and operational information. A system is disclosed that includes an interpretable machine learning model that generates a function in response to an inputted data map, wherein the data map includes feature data and operational data over a region of interest, and wherein the function relates a set of predictive variables to one or more response variables; an integration/interpolation system that generates the data map from a set of disparate data sources; and an analysis system that evaluates the function to predict outcomes at unique points in the region of interest.