Estimating Probability of Failure for Drilling Tools with Life Prediction

Abstract

Abstract Drilling tools are subject to numerous operational parameters such as revolutions per minute (RPM), vibration (lateral, stickslip and axial), pressure, torque and temperature. These parameters can greatly fatigue even the most robust tool depending on where and how the tool is operated. Lifetime prediction methodologies represent an affordable and statistically significant way to estimate the probability of failure (risk) of drilling tools in a cost effective way. Understanding the potential risk is vital to ensuring reliability, performing the most efficient maintenance on the equipment and improving drilling performance. Sophisticated risk-modeling techniques reduce uncertainty in drilling operations by making use of readily available operational field data, thus eliminating the need for costly laboratory experiments. Blind spots in the decision making process are eliminated by proactively identifying precursors to costly failures in the field. Preemptive guidance during maintenance periods, for parts that may have otherwise been overlooked based strictly on procedure, is enabled. Statistical models that relate the operating environment to component life are derived from field component failure data, and introduce a fresh way to boost the drilling tool efficiency. A Bayesian-based model selection technique is also developed which incorporates operating environment variables after each successful drilling run to dynamically select the model that gives the best survival probability, ensuring maximum utilization of a component, while avoiding failure and improving the overall reliability of the tool in the field. The implementation of lifetime prediction methodologies also leads to lowered life-cycle and maintenance costs, reduced risk and improved operational performance. The paper presents the methodology used to estimate the probability of failure of drilling tools and further illustrates how to reach risk-informed decisions.