Modelo preditivo do teor de óleos e graxas em água produzida quantificado pelo método gravimétrico

Abstract

The produced water generated by the primary oil processing carried out by offshore oil platforms, which has a total of oil and grease (TOG), is usually reinjected or disposed into the open ocean. This disposal is monitored by environmental regulatory agencies that determine maximum TOG values. In Brazil, the gravimetric method is that homologated for measuring TOG, which must be carried out in onshore laboratories. Due to the logistics of transferring samples from the platform to the laboratory, the measurement result is available approximately 20 days after the day of collection. This work proposes the development of a predictive model of gravimetric TOG (TOG-G) from process variables, added to a variable extracted from the response variable, which can be used offshore and in real time, to more quickly guide possible preventive or corrective actions in order to avoid its non-compliance. For this, the observations were grouped into clusters associated with TOG-G ranges, through which the base balancing was performed. Training and test sets were generated and a classifier was built for the cluster according to the most significant process variables for the prediction of TOG-G, identified through linear regression. Subsequently, the TOG-G was modeled from the significant process variables and the cluster. The results obtained for the test set were evaluated by means of Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), coefficient of determination (𝑅 2 ) and Pearson's correlation coefficient (𝜌), and showed to be superior both to the forecasts generated from the predictive model developed from the same forecasters, but disregarding the cluster, as to the real values of the spectrophotometric TOG (TOG-S) measurements, which constitutes the real-time method currently used as a reference in the platform. To validate the gains in accuracy with the proposed method, it was also applied to a classical set of linear regression for predicting fish weight. Thus, the inclusion of the cluster information in the TOG-G model proved to be an innovative and efficient approach to increase the accuracy of its prediction from information available on the platform, which may considerably benefit the oil industry in terms of process control.