Machine Learning Prediction of Fracture Driven Interactions
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Solution Overview
Problem
Existing methods for managing fracture driven interactions (FDI) in hydraulic fracturing operations lack real-time prediction capabilities and rely on unvalidated assumptions, leading to inefficient production and costly downtime in hydrocarbon fields.
Innovation Solution
A machine learning model utilizing real-time well treatment and offset well production data, combined with historical data, to predict and reduce FDI events by providing actionable recommendations during hydraulic fracturing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time machine learning prediction is implemented, then FDI event prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the prediction process into distinct functional components: data collection modules for well treatment and offset well production data, feature extraction layers that process the raw data, a machine learning model core that performs prediction, and output interfaces that provide FDI probability assessments. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high prediction accuracy.
Solution Approach 2:
The patent introduces intermediate feature extraction nodes that act as mediators between the raw input data and the machine learning model. These intermediate layers process and transform the well treatment data and offset well production data into feature representations that are more suitable for prediction, reducing the complexity of direct model-input connections while improving prediction performance.
2Reliability
If comprehensive data analysis is performed, then prediction reliability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary feature extraction and data processing operations before the actual prediction occurs. By pre-processing the well treatment data and offset well production data to extract meaningful features and reduce data dimensionality, the system prepares the data in advance for the machine learning model, which then can make predictions more quickly and reliably without requiring extensive real-time analysis during critical fracturing operations.
Solution Approach 2:
The patent applies partial action by selectively processing only the most relevant features and data points from the comprehensive well data, rather than analyzing every possible parameter in full detail. This selective feature extraction maintains prediction reliability by focusing on the most impactful variables while significantly reducing processing time compared to comprehensive full-data analysis.
3Productivity
If real-time prediction capability is added, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple data streams (well treatment data and offset well production data) and processing functions into a unified machine learning prediction system. By combining these elements into an integrated architecture where data collection, feature extraction, and prediction occur in coordinated real-time operations, the system improves productivity through comprehensive analysis while managing complexity through functional integration rather than separate independent systems.
Data Source
AI summary
A computing system includes a machine learning algorithm executing a machine learning model to predict a probability of a fracture driven interaction associated with a hydrocarbon well. The machine learning algorithm trains the machine learning model using well treatment pumping data, offset well production data, and well stage data. Feature extraction is performed on the pumping data, production data, and well stage data to produce a machine learning model that is used to predict the probability of a fracture driven interaction. The resulting machine learning model can be deployed for use in ongoing hydraulic fracturing operations to predict and reduce real-time fracture driven interactions.