Розробка систем прогнозного технічного обслуговування бурового обладнання на основі штучного інтелекту

The oil and gas industry relies heavily on drilling equipment operating under extreme conditions, where unexpected failures result in significant downtime, high maintenance costs, and safety risks. Traditional reactive and preventive maintenance approaches often fail to anticipate failures, leading to inefficiencies and increased operational expenses. This study presents the development and implementation of an AI-based predictive maintenance system designed to enhance the reliability of drilling equipment, reduce downtime, and optimize maintenance schedules. The system integrates machine learning algorithms, including Random Forest, Long Short-Term Memory (LSTM), and Multilayer Perceptron, with real-time data from IoT-enabled sensors monitoring parameters such as vibration, temperature, pressure, and wear. Historical maintenance logs and operational parameters are also incorporated to train models for failure prediction and remaining useful life (RUL) estimation. Data preprocessing addresses challenges like missing values and noise, while feature engineering extracts key predictors, such as anomaly detection metrics (e.g., Mahalanobis distance) and degradation trends. The system architecture supports real-time data acquisition, processing, and user-friendly visualization through a web-based interface, enabling maintenance teams to act on predictive alerts. A six-month pilot test on operational drilling rigs demonstrated a 35 % reduction in downtime (from 120 to 78 hours per month) and a 28 % decrease in maintenance costs (from $50,000 to $36,000 per month) compared to reactive maintenance. The LSTM model achieved the highest performance, with an F1-score of 0,92 for failure prediction and a mean absolute error of 10,8 hours for RUL estimation. Case studies highlighted successful predictions, such as a pump failure averted 48 hours in advance, saving 10 hours of downtime and $15,000 in repairs. Challenges include data quality issues, model interpretability, and integration with existing workflows. Compared to existing predictive maintenance systems, this approach offers superior accuracy but relies on high-quality sensor data. Future work includes incorporating edge computing for real-time processing, expanding to other equipment types, and enhancing model robustness with larger datasets and advanced algorithms like transformers. This system demonstrates the transformative potential of AI-driven predictive maintenance, offering significant cost savings, enhanced safety, and improved operational efficiency for the oil and gas industry.