OFFSHORE OIL WELLS: INDICATORS FOR CONFIGURATION SELECTION

Brazil's most productive oil fields are found in the pre-salt cluster, located at approximately 7,000 meters, with a water depth of around 2,000 meters. Offshore operations under these conditions represent a major technological challenge. If, on the one hand, the production of these wells is very attractive, generating significant revenue, on the other hand, maintenance costs and the risk of accidents are also high. In order to operate under these conditions, operators must make every effort to operate wells with a reliable integrated set of barriers and high oil and gas production availability. An important phase of the oil well's life cycle is the design. Several competing configurations are presented during this phase, and the decision-makers (DM) must choose the best option. A decision support software is under development to assist DM in this challenging task. A module of this software is intended to quantitatively assess three aspects: the integrity of the barrier elements; the reliability of production functions; and a financial indicator. This article deals with the financial indicator. The financial indicator is calculated from two sets of variables. The first set is informed by the oil and gas operator, such as the planning price of an oil barrel; the second set is from values calculated from a simulation module that uses the Monte Carlo simulation. To calculate the financial indicator, a System Dynamics model was developed. The article shows the DS model in detail, explores the model, and presents the results of an actual case used for testing, resulting in a difference of 8.9 million dollars between the two configurations. The conclusions comment on the assumption that oil and gas companies give great importance both in safety and operations continuity; also, that the result obtained for a single well may be relevant when considering a large number of wells, which is the case of great oil and gas operators; also, it is commented on the expandability of the model.