Surface Pump Pressure Prediction for Early Sand Plug Warning
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Solution Overview
Problem
Existing methods for monitoring and preventing sand plug in hydraulic fracturing operations are inadequate, providing insufficient warning time and relying heavily on manual identification, leading to inefficiencies and safety risks.
Innovation Solution
A dynamic prediction method using a CNN-LSTM-Attention hybrid neural network model integrates spatio-temporal data features to analyze fracturing treatment data, enabling real-time monitoring and multi-step predictions of bottomhole pressure changes, coupled with active independent variables for operational adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring of wellhead parameters is used, then equipment simplicity is maintained, but prediction accuracy and warning time are insufficient
Solution Approach 1:
The patent replaces traditional mechanical monitoring systems with an intelligent prediction system based on machine learning algorithms. The CNN-LSTM-Attention model processes wellhead parameter data to predict bottomhole pressure changes, substituting complex physical measurement systems with computational intelligence that achieves higher prediction accuracy without requiring additional hardware complexity.
Solution Approach 2:
The system performs preliminary prediction of bottomhole pressure changes before actual sand plug occurrence. By analyzing historical and real-time wellhead parameters through the neural network model, the system provides advance warning signals that enable preventive actions to be taken before the sand plug forms, thereby improving prediction accuracy while maintaining operational simplicity.
2Productivity
If manual identification by technical personnel is used, then system complexity is reduced, but prediction efficiency and accuracy are limited
Solution Approach 1:
The prediction system performs automated analysis of wellhead parameters using the CNN-LSTM-Attention model, eliminating the need for manual identification by technical personnel. The system self-services by automatically processing data, generating predictions, and providing warning signals, thereby dramatically improving prediction efficiency while the modular architecture keeps system complexity manageable.
Solution Approach 2:
The patent introduces an intelligent algorithm model as an intermediary between raw wellhead parameter data and operational decisions. This intermediary (the CNN-LSTM-Attention model) automatically extracts features, identifies patterns, and generates predictions, replacing manual analysis and significantly boosting prediction efficiency without requiring complex additional hardware.
3Reliability
If real-time monitoring of bottomhole pressure is implemented, then sand plug detection capability is improved, but response time is insufficient
Solution Approach 1:
The system performs preliminary prediction of bottomhole pressure changes using the CNN-LSTM-Attention model before sand plug actually occurs. By continuously analyzing wellhead parameters and predicting future bottomhole pressure trends, the system provides advance warning signals that enable operators to take preventive actions before sand plug formation, thereby improving detection capability while maintaining adequate response time.
Solution Approach 2:
The patent implements a feedback mechanism where predicted bottomhole pressure changes are continuously compared with actual measurements. This feedback loop allows the system to refine predictions, provide timely warning signals when anomalies are detected, and enable rapid response to potential sand plug conditions, thereby improving both detection capability and response time.
Data Source
AI summary
A dynamic prediction method for surface pump pressure based on spatio-temporal data features includes: S1. obtaining original fracturing treatment data and formation evaluation data of a plurality of wells; S2. extracting fracturing treatment data from a prepad fluid initiation stage to a shut-down stage in the fracturing treatment data; S3. constructing time series data with the fracturing treatment data, and construct active independent variable data by combining the fracturing treatment data and formation evaluation data; S4. establishing a hybrid neural network model and a LightGBM gradient boosting model; S5. training the hybrid neural network model and the LightGBM gradient boosting model with the time series data and the active independent variable; S6. obtaining fracturing treatment data of a new fracturing well, and predicting with the trained models; and S7. initializing weights of the prediction models, updating and iterating the weights, and obtaining a final prediction value.