A new correlation for prediction of sub-critical two-phase flow pressure drop through large-sized wellhead chokes

Abstract

Unacceptable errors resulted from utilizing the current correlations for investigating the behavior of sub-critical two-phase flow regime through wellhead chokes of south Iranian gas condensate reservoirs led us toward establishing a new Gilbert-type correlation capable of fitting the production data with minimum errors. The proposed model, which is a modification of Nasriani and Kalantariasl model, is able to predict the high flow rates of gas condensate wells under sub-critical conditions particularly in case of large choke sizes. In order to validate the new correlation, Genetic Algorithm and non-linear regression analysis methods are implemented to sixty seven production datasets of fifteen wells with large wellhead choke sizes (40/64–192/64 inch) gathered from 10 different fields. Then the proposed correlation in addition to two other models (1.Osman and Dokla 2. Nasriani and Kalantariasl) are conducted to each choke size to investigate the applicability of the new formula in comparison with the existing ones. Moreover, in order to evaluate the new model in other field data, 39 data points gathered from gas-condensate wells of Fars province of Iran are exposed to the proposed model, and the two other models. Finally, the main form of the new correlation is applied to dataset of each choke size.The results indicate that the non-linear regression technique is more accurate than Genetic algorithm in fitting the data to the proposed method. Furthermore, the new correlation has the minimum errors in comparison to other methods in both investigated areas. Finally, according to statistical error analysis for each choke size, the ability of the proposed correlation to predict gas flow rates of fluids passing through the wellhead chokes of gas-condensate wells under sub-critical conditions is found to be highly improved when applied to individual choke sizes.