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A rate of penetration (ROP) prediction method based on improved dung beetle optimization algorithm and BiLSTM-SA.
Mengyuan Xiong1,2, Shuangjin Zheng3,4, Wei Liu5
1School of Petroleum Engineering, National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan, 430100, China.
Scientific Reports
|October 29, 2024
Summary
This study introduces an advanced model for predicting oil drilling Rate of Penetration (ROP). The BiLSTM-SA-IDBO model significantly enhances prediction accuracy and efficiency, outperforming traditional methods.
Area of Science:
- Petroleum Engineering
- Artificial Intelligence in Drilling
Background:
- Accurate Rate of Penetration (ROP) prediction is vital for optimizing oil drilling operations and reducing costs.
- Existing prediction models often suffer from limitations in accuracy and generalization, hindering practical application.
Purpose of the Study:
- To develop an end-to-end ROP prediction model with superior accuracy and generalization capabilities.
- To integrate Bidirectional Long Short-Term Memory (BiLSTM), Self-Attention (SA), and an Improved Dung Beetle Optimization algorithm (IDBO) for enhanced ROP prediction.
Main Methods:
- Proposed an end-to-end ROP prediction model, BiLSTM-SA-IDBO, incorporating the Bingham physical equation.
- Enhanced the Dung Beetle Optimization (DBO) algorithm using Sobol sequences, Golden Sine algorithm, and dynamic subtraction factors to create IDBO.
- Optimized the BiLSTM-SA model using the developed IDBO algorithm.
Main Results:
- The BiLSTM-SA-IDBO model achieved excellent performance with RMSE of 0.065, R² of 0.963, and MAE of 0.05 on the test set.
- Demonstrated significant improvements over the original BiLSTM-SA model, with error metrics reduced by up to 83%.
- Outperformed traditional models including BP Neural Network, Random Forest, XGBoost, and LSTM in predictive accuracy.
Conclusions:
- The proposed BiLSTM-SA-IDBO model exhibits superior predictive accuracy and generalization capabilities for ROP prediction.
- Practical testing validated the model's effectiveness, indicating its strong potential for real-world oil drilling applications.
- The integration of advanced AI techniques offers a promising direction for improving drilling efficiency and cost-effectiveness.
