The capability to optimize drilling performance by predicting the life of drilling components is integral to preventing costly downhole tool failures and ensuring success of any drilling operation. Drilling tools are subject to various parameters such as vibration, temperature, revolutions per minute (RPM) and torque. These parameters can greatly fatigue even the most robust tool depending on the where and how the tool is operated. As a result, there is a need to predict time to failure of components operating in a downhole drilling environment. Analyzing operational data, inclusive of the parameters above, prior to or during maintenance actions and before starting drilling jobs, provides unique insight into how to improve the drilling performance of tools and to reduce downtime. Life prediction provides a cutting-edge way to identify precursors to costly failures in the field and enables proactive guidance during maintenance periods for parts which may otherwise have been disregarded strictly on maintenance procedures. Statistical models that relate operating environment to the component life and are derived from failure data of fielded components, introduce a new way to optimize the efficiency of drilling tools. Utilizing lifetime prediction to optimize drilling performance is a groundbreaking methodology developed to determine life of components operating in benign and harsh drilling environments by incorporating statistical aspects such as those caused because of variation in operating stress and maintenance upgrades. Since the algorithm utilizes field data, the need for costly laboratory experiments are also eliminated. Each model developed is unique to the specified part and can be calibrated for the best fit. In this methodology, a Bayesian-based model selection technique is developed that incorporates operating environment variables after each successful drilling run to dynamically select a model that gives the best survival probability for that component. Dynamic model selection ensures maximum utilization of a component, while avoiding failure to improve the overall reliability of the tool while in the field. The paper describes the methodology used to estimate the life of components in drilling systems by employing operational data, drilling dynamics and historical information.

This paper introduces an analytical approach, Probability and Confidence Trade-space (PACT), which can be used to assess uncertainty in International Space Station (ISS) hardware sparing necessary to extend the life of the vehicle. There are several key areas under consideration in this research. We investigate what sparing confidence targets may be reasonable to ensure vehicle survivability and for completion of science on the ISS. The results of the analysis will provide a methodological basis for reassessing vehicle subsystem confidence targets. An ongoing annual analysis currently compares the probability of existing spares exceeding the total expected unit demand of the Orbital Replacement Unit (ORU) in functional hierarchies approximating the vehicle subsystems. In cases where the functional hierarchies' availability does not meet subsystem confidence targets, the current sparing analysis further identifies which ORUs may require additional spares to extend the life of the ISS. The resulting probability is dependent upon hardware reliability estimates. However, the ISS hardware fleet carries considerable epistemic uncertainty (uncertainty in the knowledge of the true hardware failure rate), which does not currently factor into the annual sparing analysis. The existing confidence targets may be conservative. This paper will also discuss how confidence targets may be relaxed based on the inclusion of epistemic uncertainty for each ORU. The paper will conclude with strengths and limitations for implementing the analytical approach in sustaining the ISS through end of life, 2020 and beyond.

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.

The capability to predict performance and lifetime of drilling electronics is the key to preventing costly downhole tool failures and ensuring success of any drilling operation. Drilling electronics operate under extremely harsh downhole environments with temperatures beyond 150C and vibration levels exceeding 15g. In addition to temperature and vibration, there are several factors affecting electronic reliability that have high uncertainty and cannot be accurately measured. There is a growing trend in the oil and gas industry to drill faster and operate at higher temperatures and pressures, forcing tools to operate beyond design specifications. This has resulted in increased failure rate leading to higher maintenance costs and system downtime for drilling operators as well as service providers. This paper develops a methodology to estimate the life of drilling electronics by using operational data, drilling dynamics and historical maintenance information. The methodology combines parameter estimation techniques, statistical reliability analysis and Bayesian math in a probabilistic framework. Parameter estimation is used to calibrate statistical equations to field data and probabilistic analysis is used to obtain the likelihood of failure. In the paper, the model parameters are represented as random variables, each with a probability distribution. Drilling electronics under downhole conditions can have several failure modes and each failure mode can be caused by the interaction of several variables. When information on each failure mechanism is not readily available, the failure is expressed in terms of several candidate models. Bayesian updating is used to incorporate real time operational history for a specific part and select the most accurate failure model for that part. Tis is for the first time, a systematic approach is developed for predicting the life of electronics in downhole drilling environments using statistical modeling and probabilistic methods on life cycle history and operational data from the field.