Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study

Abstract

None of the various published models used to predict oil production rates through wellhead chokes from fluid composition and pressures can be considered as a universal model for all regions. Here, a model is provided to predict liquid production-flow rates for the Reshadat oil field offshore southwest Iran, applying a customized genetic optimization algorithm (GA) and standard Excel Solver non-linear and evolutionary optimization algorithms. The dataset of 182 records of wellhead choke measurements spans liquid flow rates from < 100 to 30,000 stock tank barrels/day. Each data record includes measurements of five variables: liquid production rate (QL), wellhead pressure, choke size, basic sediment and water, and gas–liquid ratio. 70% of the dataset (127 data records) was used for training purposes to establish the prediction relationships, and 30% of the dataset (55 data records) was utilized for independently testing the accuracy of the derived relationships as predictive tools. The methodology applying either the customized GA or standard Solver optimization algorithms, demonstrates significant improvements in QL-prediction accuracy with the lowest APD (− 7.72 to − 2.89), AAPD (7.33–8.51), SD (288.77–563.85), MSE (91,871–316,429), and RMSE (303.1–562.52); and the highest R2 (greater than 0.997) compared to six previously published liquid flow-rate prediction models. As a general result, the novel methodology is easily applied to other field/reservoir datasets, to achieve rapid practical flow prediction applications, and is consequently of worldwide significance.