A Probabilistic Approach for Reliability and Life Prediction of Electronics in Drilling and Evaluation Tools

Amit Kale, Katrina Carter-Journet, Troy Falgout, Ludger Heuermann-Kuehn, and Derick Zurcher
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Full Paper
phmc_14_054.pdf997.24 KBSeptember 17, 2014 - 4:03am

The capability to predict performance and life of drilling electronics is the key for preventing costly downhole tool failures and ensuring the success of any drilling operation. Drilling electronics operate under extremely harsh downhole environment with temperatures beyond 200° C and vibration levels exceeding 15 g. In addition to temperature and vibration there are several factors affecting reliability of electronics that have high uncertainty and cannot be accurately measured. In recent years there has been growing trend in the oil and gas industry to drill faster as well as operate in high temperatures and pressures, causing the tools to operate beyond design specifications. This trend has led to high maintenance cost 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. The parameter estimation technique is used to calibrate statistical equations to field data, and probabilistic analysis is used to obtain the likelihood of failure. Here, the model parameters are represented as random variables, each with a probability distribution. Drilling electronics in downhole conditions can have several failure modes, and each failure mode can be caused by the interaction of several variables. Information on each failure mechanism is not readily available so we express the failure in terms of several candidate models. Bayesian updating is used to incorporate an operational run history for a specific part and to select the most accurate failure model for that part. This is the first time a systematic method has been developed for predicting the life of electronics in a downhole drilling environment using statistical modeling and probabilistic methods on life cycle history and operational data from the field.

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Submission Keywords: 
predictive analytics
applications: electronics
Bayesian updating
Submission Topic Areas: 
CBM and informed logistics
Data-driven methods for fault detection, diagnosis, and prognosis
Modeling and simulation
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