The production optimization in Oil & Gas fields is an activity which relies on a wide range of parameters and variables, where many of them can vary over time due to production trends and inherent uncertainty. However, while developing the optimal strategy for oil production, it is usual to ignore those uncertainties, which can affect the optimum operational point, leading to disappointment and loss of expected production. This paper aims to analyze the influence of the water cut (BSW) into the gas lift optimization process. Statistical data from producer wells are combined with multiphase flow correlations to estimate the uncertainty in the production variables. A mathematical optimization model is built using the Mixed Integer Linear Programming Technique (MILP), linearizing the oil well performance curves. For each uncertain scenario the optimization model was run. A case study with representative data shows that the uncertainties importance grows in the most constrained scenarios and how the absence of uncertainty can overestimate the expected oil production.

The development of digital transformation tools has significantly increased the amount of available data in recent years. As in all sectors of the economy, the oil industry, with a focus on well construction, was not left out of the process. Numerous data are monitored and stored in real time and several studies are carried out to increase operational performance. However, little has evolved in relation to the knowledge that these data can generate in decision-making processes, such as in the definition of targets that serve as a reference for the planning of interventions. Thus, this article explains a new methodology for defining targets, based on the knowledge of specialists, the concept of zero-based budgeting and risk analysis, with the integration of teams of well designers, reservoirs, and budgeting. The methodology was used in 19 fields’ studies and directly applied to the planning of 5 new projects.