Bayesian prior distribution based on generic data and experts’ opinion: A case study in the O&G industry

Abstract

Reliability estimation is an essential task to ensure predictability on the life of equipment installed in oil wells, allowing forecasting costs, planning maintenance, and estimating system availability. However, data may be rather scarce and/or expensive to obtain, especially for technologies under development in the Oil and Gas (O&G) industry. The information is frequently available in generic databases and expert opinions. In the Bayesian framework, the prior knowledge about a system's reliability is updated as a new field and/or test data are gathered. This paper proposes an approach that does not require direct elicitation of parameters to define informative prior distributions for the reliability function using fault tree analysis, expert's opinions, and/or generic data at the system level of equipment under development. Specifically, the method-of-moments and maximum-entropy are adopted to propagate downward the uncertainty from the system level through the failure modes until the basic events of the fault tree, weighted by the expert knowledge of the failure behavior. Then, in possession of all prior distributions of the basic events, the information may be propagated upward based on Monte Carlo simulation to update the system reliability distribution. Finally, we present a case study of the proposed methodology applied to a novel open-hole expandable packer, a completion equipment recently installed in a Brazilian oil field. The failure modes and failure causes are discussed, and, after expert elicitation, the definition of the informative prior distribution is achieved. Therefore, we estimate the reliability for a given time and assess if the novel equipment attains the company's risk target, taking O&G standards as reference.