A combined support vector regression with firefly algorithm for prediction of bottom hole pressure

Abstract

Bottom hole pressure (BHP) is a fundamental parameter for the proper design of the production process and the development of reservoirs. BHP can be measured directly through the deployment of pressure down-hole gauges (PDG) or by the application of existing correlations and mechanistic models based on surface measurements. Unfortunately, these methods suffer from two main problems: the cost of measurement which is quite expensive mainly for PDG, and the inaccuracies for the correlations and mechanistic models, due to the limitation of their ranges of application. In this work, a new model based on support vector regression (SVR) optimized with firefly algorithm (FFA) is proposed to predict BHP of vertical wells with multiphase flow. Firefly algorithm is implemented for the optimal selection of SVR hyper-parameters. SVR-FFA model development is done using real-life measurement datasets obtained from distinct Algerian oil wells. The performance of the SVR-FFA model is compared with another hybridization SVR-genetic algorithm, trial and error SVR and with existing correlations and mechanistic models. The results demonstrate that the SVR-FFA model outperforms all the other models.