Prediction of a wellhead separator efficiency and risk assessment in a gas condensate reservoir

Abstract

In this paper artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were applied to predict the separation percentage of gas and gas condensate in a wellhead separator in Naar oil field (Boushehr province, IRAN). The operating parameters including valve opening percentage, gas flow, design pressure, and design temperature are considered as the inputs of the models. The accuracy of the proposed models were evaluated using statistical parameters such as correlation coefficient (R2), average percent relative error (APRE), average absolute percent relative error (AAPRE), and root mean square error (RMSE). Based on the achieved data, R2 values were 0.9691 and 0.9807 for ANN and ANFIS models, respectively, while the values of RMSE were 6.117 and 4.57 for the applied models, which denote the higher accuracy of ANFIS model. Moreover, risk analyzing and consequence assessment of probable explosion of separator using PHAST (Process Hazard Analysis Software) software showed that inspection of separators is very important. Considering the calculated results, it can be concluded that ANFIS was better than ANN in prediction of gas and gas condensate separation percentages, since its output showed higher affinity to the real data. Generally, the findings obtained from the current work suggest that it is possible to predict the separation efficiency of a wellhead separator using intelligent systems.