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A Random Forest-Based Method for Predicting Borehole Trajectories

Author

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  • Baoyong Yan

    (State Key Laboratory of the Gas Disaster Detecting, Preventing and Emergency Controlling, Chongqing 400039, China
    CCTEG Chongqing Research Institute, Chongqing 400039, China)

  • Xiantao Zhang

    (State Key Laboratory of the Gas Disaster Detecting, Preventing and Emergency Controlling, Chongqing 400039, China
    CCTEG Chongqing Research Institute, Chongqing 400039, China)

  • Chengxu Tang

    (College of Artificial Intelligence, Southwest University, Chongqing 400715, China)

  • Xiao Wang

    (College of Artificial Intelligence, Southwest University, Chongqing 400715, China)

  • Yifei Yang

    (College of Artificial Intelligence, Southwest University, Chongqing 400715, China)

  • Weihua Xu

    (College of Artificial Intelligence, Southwest University, Chongqing 400715, China)

Abstract

Drilling trajectory control technology for near-horizontal directional drilling in coal mines is mainly determined empirically by manual skew data, and the empirical results are only qualitative and variable, meanwhile possessing great instability and uncertainty. In order to improve the accuracy and efficiency of drilling trajectory prediction, this paper investigates a random forest regression-based drilling trajectory prediction method after applying numerous machine learning methods to experimental data for comparison. In the selection of prediction features, this paper replaces all feature variables of the borehole trajectory with feature values with higher cumulative influence weights, and screens out three variables with high importance, azimuth, inclination and bend at the present moment of the drill, as the input variables of the model, and the increments in the borehole in a horizontal direction, left and right direction, and up and down direction at the present moment and the next moment as the output variables of the model. In the model training, the model in this paper uses the bootstrap self-service method resampling technique to collect training sample data, constructs a random forest regression model, and takes the mean value of the decision tree output as the result of the borehole trajectory prediction. To further improve the model accuracy, the paper improves the prediction performance of the model by adjusting the parameters of the random forest model such as tree, depth, minimum sample of leaf nodes, minimum sample number of internal node division, etc. The model is also evaluated by using common machine learning evaluation metrics R2 score, RAE, RMSE and MSE. The experimental results show that the average absolute error of the model drops to 1% on the prediction of the horizontal direction and up and down direction; the average absolute error of the model drops to 9% on the prediction of the left and right direction, and this error rate reaches the error requirement in the actual construction process, so the model can effectively improve the prediction accuracy of borehole trajectory while improving the safety of directional construction.

Suggested Citation

  • Baoyong Yan & Xiantao Zhang & Chengxu Tang & Xiao Wang & Yifei Yang & Weihua Xu, 2023. "A Random Forest-Based Method for Predicting Borehole Trajectories," Mathematics, MDPI, vol. 11(6), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1297-:d:1090908
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    References listed on IDEAS

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    1. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
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    1. Jun Shu & Xinyu Xia & Suyue Han & Zuli He & Ke Pan & Bin Liu, 2024. "Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-23, May.
    2. Wang, Miaomiao & Wang, Yanfu & Ding, Jie & Yu, Weizhe, 2024. "Interaction aware and multi-modal distribution for ship trajectory prediction with spatio-temporal crisscross hybrid network," Reliability Engineering and System Safety, Elsevier, vol. 252(C).

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