A New Correlation for Prediction of Critical Two-phase Flow through Wellhead Chokes

Abstract

The first scope of this study is to develop a new accurate empirical Gilbert type critical flow correlation based on 361 actual production tests data from Middle Eastern oil fields by means of non-linear regression analysis. The second scope is to study the impact of temperature on Gilbert type critical flow correlation for these data sets. In order to modify the Gilbert type critical flow correlation for these data sets, correlations are tuned based on available field data points using nonlinear regression method. in this study, generalized reduced gradient (GRG) algorithm of iteration was used to find the correlation coefficients based on available field data and the convergence criteria is to minimize the value of the squared sum, SS, of the difference between the real data and the estimated one. Based on error analysis, for the oil fields, liquid flow rate prediction is improved when new approaches (including/excluding temperature) are used. It is also concluded that the accuracy of new approach to predict production rate is not expressively improved by including the temperature in choke performance correlation for this data set compared to the case without temperature.