Risk Estimation of Oil and Gas Industry Equipment

Abstrak

The oil and gas industry is an essential part of the global energy supply. Offshore exploration and production are important. In drilling, both onshore and offshore, the Bottom Hole Assembly (BHA) plays a crucial role. Logging While Drilling (LWD) tools depend on electronic boards that are very sensitive to downhole conditions like high pressure, vibration, and temperature changes. When these boards fail, it can disrupt operations, increase costs, and affect safety. Traditional maintenance methods struggle because they can't respond to changing risk conditions. This study creates a probabilistic framework using the Hidden Markov Model (HMM) to estimate the failure risk of LWD electronic boards. The research uses operational data, including temperature, pressure, vibration severity, usage, and fault records as observation sequences. The model is trained with the Baum-Welch algorithm, which helps identify hidden degradation states and generate time varying failure probabilities. Results are divided into four risk levels: low, moderate, high, and extreme. This classification provides a clear risk profile for proactive monitoring. The findings indicate that HMM effectively captures degradation transitions. It also offers early warnings and improves the reliability assessment of LWD tools, potentially benefiting offshore asset integrity management.