Enhancing Drilling Equipment Reliability: Deep Learning for Predicting Failure Time and Real-Time Anomaly Detection

Abstract Predict drilling equipment's failure time and real-time anomaly detection play crucial roles in ensuring the seamless Oil and Gas wells drilling operations. In this study, we delve into the application of neural network-based machine learning techniques specifically tailored for these tasks in drilling. Our primary objectives encompass diagnosing equipment health states, detecting anomalies in real-time, and predicting remaining useful life (RUL). To address these challenges, we implement a novel combination of neural network architectures, including convolutional layers, Long Short-Term Memory (LSTM) cells, and attention layers. By meticulously training our model on historical data using carefully selected deep-learning hyperparameters, we tackle the unique characteristics of drilling equipment data. The resulting neural network predicts total lifetime and RUL based on historical input. Our LSTM model achieved an accuracy of 74.03%, given the complexity and novelty of the dataset, these results establish a strong benchmark for future research. This improvement results from hyperparameter tuning and optimized network architectures. Additionally, accurate anomaly detection and minimized unscheduled downtime of 14% demonstrate potential cost savings for oilfield operators. By identifying potential failures in advance, our approach allows efficient scheduling of maintenance activities. Field engineers express satisfaction with our approach, validating its effectiveness. Our research introduces novel aspects to drilling equipment maintenance. Through empirical validation, we rigorously test our approach using real-world drilling sensor data, ensuring robustness and reliability. This instills confidence in industry practitioners. Furthermore, our intuitive real-time anomaly detection system enhances drilling safety, efficiency, and cost savings.