LSTM Recurrent Neural Network Based Method for Identification of PDC Drill Bit Operating Conditions and Its Application

Abstract PDC drill bit underground operating conditions identification is one of of the difficulties during the drilling operation. It is of great significant for the drilling improvement and accurate construction formation to accurately identify the complex operating conditions during drilling the operation such as PDC bit wearing and stick-slip vibration, etc. In this paper, data of relationship between torque, rate of penetration (ROP) signal and weight on bit (WOB), rev were obtained through lab simulation experiment, after which, a relationship database of torque, ROP signal and WOB, rev was established, a single-layer LSTM recurrent neural network (LSTM-RNN) was constructed, the number of neurons, batch size and optimizer type of LSTM layer were optimized. With BPTT algorithm, LSTM model was trained and tested on the training set containing 6000 groups of data, and achieved the goal of identifying the bit operation conditions against the input of WOB, torque, rev and ROP signal parameters. Based on this model, the PDC bit underground operating conditions identification software was compiled, and was connected to the logging unit to realize real-time identification of bit underground operating conditions, and field test application was successfully carried out in Mahu 1 Well Region. The research showed that the convergence speed was fast and the time cost was at the lowest when the number of neurons of LSTM model was 50, and that the optimized Adam optimizer model could meet the requirements of convergence speed and prediction accuracy with the lowest loss value. The classification accuracy of this model on the test set reached 94% and the accuracy of the prediction method was also verified by field test application. To sum up, the method of LSTM-RNN based PDC bit underground operating conditions real-time identification can better meet the requirements of accurate identification of PDC bit underground operating conditions in the practical drilling, and has guiding significance for the judgement of PDC bit operating conditions on site and formulation of scientific construction decisions.

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Publication Details

Journal
Journal of Physics Conference Series
Published
2024-10-01
DOI
https://doi.org/10.1088/1742-6596/2834/1/012097
Citations
1
Primary Topic
Advanced machining processes and optimization
Type
article
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0.21
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LSTM Recurrent Neural Network Based Method for Identification of PDC Drill Bit Operating Conditions and Its Application

Guodong Ji, Zehao Lv, Changchang Chen, Hongyuan Zhang et al.
1 citations
Journal of Physics Conference Series
Advanced machining processes and optimization
0.21
article

LSTM Recurrent Neural Network Based Method for Identification of PDC Drill Bit Operating Conditions and Its Application

Guodong Ji, Zehao Lv, Changchang Chen, Hongyuan Zhang, Yuqi Sun, Jinping Yu
article en
1 citations

Abstract

Abstract PDC drill bit underground operating conditions identification is one of of the difficulties during the drilling operation. It is of great significant for the drilling improvement and accurate construction formation to accurately identify the complex operating conditions during drilling the operation such as PDC bit wearing and stick-slip vibration, etc. In this paper, data of relationship between torque, rate of penetration (ROP) signal and weight on bit (WOB), rev were obtained through lab simulation experiment, after which, a relationship database of torque, ROP signal and WOB, rev was established, a single-layer LSTM recurrent neural network (LSTM-RNN) was constructed, the number of neurons, batch size and optimizer type of LSTM layer were optimized. With BPTT algorithm, LSTM model was trained and tested on the training set containing 6000 groups of data, and achieved the goal of identifying the bit operation conditions against the input of WOB, torque, rev and ROP signal parameters. Based on this model, the PDC bit underground operating conditions identification software was compiled, and was connected to the logging unit to realize real-time identification of bit underground operating conditions, and field test application was successfully carried out in Mahu 1 Well Region. The research showed that the convergence speed was fast and the time cost was at the lowest when the number of neurons of LSTM model was 50, and that the optimized Adam optimizer model could meet the requirements of convergence speed and prediction accuracy with the lowest loss value. The classification accuracy of this model on the test set reached 94% and the accuracy of the prediction method was also verified by field test application. To sum up, the method of LSTM-RNN based PDC bit underground operating conditions real-time identification can better meet the requirements of accurate identification of PDC bit underground operating conditions in the practical drilling, and has guiding significance for the judgement of PDC bit operating conditions on site and formulation of scientific construction decisions.

Journal of Physics Conference SeriesVol. 2834(1)
China National Petroleum Corporation (China) (CN)
Openalex Percentile: Top 50%
Advanced machining processes and optimization
0.21
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