Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. Flow rates of phases (oil, gas and water) are most important parameter which is detected by MPFMs. Conventional MPFM data collecting is done in long periods; because of radioactive sources usage as detector and unmanned location due to wells far distance. In this paper, based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented. Temperatures and pressures of lines have been set as input variable of network and oil flow rate as output. In this case a 1600 data set of 50 wells in one of the northern Persian Gulf oil fields of Iran were used to build a database. ICA-ANN can be used as a reliable alternative way without personal and environmental problems. The performance of the ICA-ANN model has also been compared with ANN model and Fuzzy model. The results prove the effectiveness, robustness and compatibility of the ICA-ANN model.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► New approach for estimating oil flow rate has been presented. ► ICA-ANN model combines local and global searching ability of the ANN and ICA, respectively. ► It has improved the fitting between ICA-ANN prediction of the model and the measured values. ► The ICA parameters of the algorithm have been carefully designed to optimize the neural network, avoiding premature convergence.