Automatic Implementation Algorithm of Pressure Relief Drilling Depth Based on an Innovative Monitoring-While-Drilling Method

• Main Authors: Li, C. (Author), Wu, Z. (Author), Zhang, W.-L (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Deep neural networks; drilling depth; Drilling depth; Drilling methods; drilling state identification algorithm; Drilling state identification algorithm; Identification algorithms; Infill drilling; Long short-term memory; monitoring-while-drilling method; Monitoring-while-drilling method; neural network; Neural network model; Neural-networks; State identification; Vibration signal; vibration signals; While drillings
• Online Access: View Fulltext in Publisher

 An innovative monitoring-while-drilling method of pressure relief drilling was proposed in a previous study, and the periodic appearance of amplitude concentrated enlargement zone in vibration signals can represent the drilling depth. However, there is a lack of a high accuracy model to automatically identify the amplitude concentrated enlargement zone. So, in this study, a neural network model is put forward based on single-sensor and multi-sensor prediction results. The neural network model consists of one Deep Neural Network (DNN) and four Long Short-Term Memory (LSTM) networks. The accuracy is only 92.72% when only using single-sensor data for identification, while the proposed multiple neural network model could improve the accuracy to being greater than 97.00%. In addition, an optimization method was supplemented to eliminate some misjudgment due to data anomalies, which improved the final accuracy to the level of manual recognition. Finally, the research results solved the difficult problem of identifying the amplitude concentrated enlargement zone and provided the foundation for automatically identifying the drilling depth. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.