A Bayesian Population Variability-Based Methodology for Reliability Assessment in the Oil and Gas Industry

Scarcity of historical failure data is very common in many situations, especially in the Oil and Gas (O&G) industry. In this context, the Bayesian analysis is paramount to obtain reliable estimates for the system of interest. To perform this analysis, we propose using the Bayesian population variability analysis in a two-step approach. Such an approach allows the assessment of the variability of reliability measures among a similar population of systems. The first step is based on the prior estimation, and it involves gathering available data from similar systems (generic data) and constructing the prior distributions, that represents the population variability. This prior information consists of data of systems that exhibit similar, yet different reliability behavior. In the second step, one can proceed to posterior estimation, where the prior distribution is updated with the available evidence from the system of interest. To obtain the posterior estimates, Markov Chain Monte Carlo-based methods are required. In this work, we illustrate this approach assuming systems with non-constant failure rates, and the model was validated using synthetic data and the results indicate the usefulness of this approach in the O&G industries to better address the reliability measures of its systems.

Keywords: Bayesian analysis, Particle swarm optimization, Markov Chain Monte Carlo, Oil & gas industry.

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