A semi-Markov model with Bayesian belief network based human error probability for availability assessment of downhole optical monitoring systems

Compelled by increasing oil prices, a research effort is underway for designing and implementing intelligent oil fields in Brazil, with a first pilot directed towards mature wells in the Northeast. One of the major benefits of this technology is the anticipation of oil production volumes and an improved reservoir management and control. Given the considerable steep investment on the new technology, availability is a key attribute: higher availability means higher production volumes. An important part of this effort is the development of pressure–temperature optical monitoring systems (OMS) and their availability assessment. Availability analysis of an OMS impose some complexities, where the most relevant aspects are: (i) the system is under a deteriorating process; (ii) the available time to complete the maintenance; and (iii) human error probability (HEP) during maintenance that is influenced by the available time and other factors (e.g., experience, fatigue) in returning an OMS to its normal operational condition. In this paper we present a first attempt to solve this problem. It is developed an availability assessment model in which the system dynamics is described via a continuous-time semi-Markovian process specified in terms of probabilities. This model is integrated with a Bayesian belief network characterizing the cause-effect relationships among factors influencing the repairman error probability during maintenance. The model is applied to a real case concerning mature oil wells.