TY  - JOUR
AU  - Qiao, Ying
AU  - Xu, Hong-Min
AU  - Zhou, Wen-Jun
AU  - Peng, Bo
AU  - Hu, Bin
AU  - Guo, Xiao
PY  - 2023
TI  - A BiGRU joint optimized attention network for recognition of drilling conditions
JO  - Petroleum Science
SN  - 1672-5107
SP  - 3624
EP  - 3637
VL  - 20
IS  - 6
AB  - The identification and recording of drilling conditions are crucial for ensuring drilling safety and efficiency. However, the traditional approach of relying on the subjective determination of drilling masters based on experience formulas is slow and not suitable for rapid drilling. In this paper, we propose a drilling condition classification method based on a neural network model. The model uses an improved Bidirectional Gated Recurrent Unit (BiGRU) combined with an attention mechanism to accurately classify seven common drilling conditions simultaneously, achieving an average accuracy of 91.63%. The model also demonstrates excellent generalization ability, real-time performance, and accuracy, making it suitable for actual production. Additionally, the model has excellent expandability, which enhances its potential for further application.
UR  - https://doi.org/10.1016/j.petsci.2023.05.021
DO  - 10.1016/j.petsci.2023.05.021