In general, flow measurement systems in production units only report the daily total production rates. As there is no precise control of individual production of each well, the current well flow rates and their parameters are determined when production tests are conducted. Because production tests are performed periodically (e.g., once a month), information about the wells is limited and operational decisions are made using data that are not updated. Meanwhile, well properties and parameters from the production test are typically used in multiphase flow models to forecast the expected production. However, this is done deterministically without considering the different sources of uncertainties in the production tests. This study aims to introduce uncertainties in oil flow rate forecast. To do this, it is necessary to identify and quantify uncertainties from the data obtained in the production tests, consider them in production modeling, and propagate them by using multiphase flow simulation. This study comprises two main areas: data analytics and multiphase flow simulation. In data analytics, an algorithm is developed using R to analyze and treat the data from production tests. The most significant stochastic variables are identified and data deviation is adjusted to probability distributions with their respective parameters. Random values of the selected variables are then generated using Monte Carlo and Latin Hypercube Sampling (LHS) methods. In multiphase flow simulation, these possible values are used as input. By nodal analysis, the simulator output is a set of oil flow rate values, with their interval of occurrence probabilities. The methodology is applied, using a representative Brazilian offshore field as a case study. The results show the significance of the inclusion of uncertainties to achieve greater accuracy in the multiphase flow analysis of oil production.

The oil and gas industry is faced with uncertainty in many activities. Whereas in many of its areas, such as finance, geology and reservoirs these uncertainties are already incorporated in the modelling and studies. This is not the reality when it comes to modelling artificial lift and multiphase flow. Studies related with uncertainties in oil and gas production are limited. Therefore, this paper aims to develop a methodology to identify and quantify uncertainties, obtaining thus more accurate data to be used in production modelling. In addition, the methodology intends to evaluate oil production forecasting considering uncertainty propagation in oil flow simulation software. This study was divided in statistical analysis and production forecasting. An algorithm using R software analyzed and treated production data. It was applied statistical modelling techniques to data series. Deviations from these data were adjusted to a continuous distribution that provides the parameters to be used by Monte Carlo simulation method to generate random values to be input uncertainties of the MARLIM multiphase flow simulator. Oil flow rate, as output simulator, was adjusted to a new distribution and finally the intervals of occurrence probabilities of oil flow rate forecasts. This methodology was applied to BSW (Basic Sediments and Water) data from a representative field, showing the importance of including uncertainty analysis in order to generate greater reliability and accuracy in the production flow modeling. In conclusion, the method presented excellent results when applied to BSW.

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.