Decision Tree Model for Well Performance Prediction
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Solution Overview
Problem
Current methods for predicting and optimizing the performance of oil and natural gas wells are limited by their inability to accurately account for dynamic factors and time-dependent features, leading to inefficiencies in well location, drilling costs, and operational safety.
Innovation Solution
A decision-tree-based model that incorporates both time-independent and time-dependent input features, trained on historical well production data, to predict and explain well performance, allowing for more accurate pre-drilling and post-drilling operations by identifying key factors influencing productivity, cost, and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static parameter models are used for well performance prediction, then the model structure is simple, but the predictive accuracy is insufficient due to inability to account for dynamic factors and time-dependent features
Solution Approach 1:
The patent transforms static parameter models into dynamic models by incorporating time-dependent features and operational data that evolve over time. The system uses historical operational data from multiple time points to capture dynamic well performance characteristics, enabling the model to adapt to changing conditions while maintaining predictive accuracy.
Solution Approach 2:
The patent implements feedback mechanisms by using historical operational data and actual well performance outcomes to continuously refine and update the predictive model. The system learns from past performance patterns and uses this feedback to improve future predictions, addressing the complexity-accuracy tradeoff through iterative optimization.
2Measurement precision
If more time-dependent features and operational data are incorporated into the model, then the predictive accuracy improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex dataset into distinct time-dependent features and time-independent parameters. By organizing operational data into structured time-series components and categorizing features by their temporal characteristics, the system simplifies the processing of complex multi-dimensional data while maintaining comprehensive predictive capabilities.
Solution Approach 2:
The patent introduces intermediate processing layers that transform raw operational data into meaningful features before feeding them into the predictive model. These intermediary processing steps include data normalization, feature engineering, and temporal pattern extraction, which reduce the complexity of raw data while preserving the essential information needed for accurate predictions.
3Adaptability or versatility
If comprehensive well performance modeling is performed for both pre-drilling and post-drilling operations, then the versatility of the model increases, but the computational resources and processing time required increase
Solution Approach 1:
The patent performs preliminary model training and feature extraction during off-peak periods using historical data, so that when actual prediction needs arise, the model is already prepared and can provide rapid predictions. This preliminary action reduces the processing time required during critical decision-making moments while maintaining comprehensive modeling capabilities.
Solution Approach 2:
The patent dynamically adjusts model parameters and complexity based on the specific prediction task and available data. For pre-drilling predictions, the model uses static parameters and geological data, while for post-drilling operations, it incorporates operational data with varying levels of detail, optimizing computational efficiency for each scenario while maintaining versatility.
Data Source
AI summary
A system may include persistent storage containing training data related to well production, wherein entries in the training data respectively include time-independent input feature values and time-dependent input feature values both mapped to ground-truth production values of corresponding wells at particular points in time, wherein the time-dependent input feature values include ground-truth production values of the corresponding wells at respectively earlier points in time. The system may also include one or more processors configured to: train a decision-tree-based model with the training data; provide, to the decision-tree-based model, new time-independent input feature values and new time-dependent input feature values for a well; and receive, from the decision-tree-based model, one or more predicted production values of the well, wherein the one or more predicted production values are generated by the decision-tree-based model based on its internal structure, the new time-independent input feature values, and the new time-dependent input feature values.