Application of artificial neural network for predicting production flow rates of gaslift oil wells

In petroleum industry, the prediction of oil production flow rate plays an important role in tracking the good performance as well as maintaining production flow rate. In addition, a flow rate modelling with high accuracy will be useful in optimizing production properties to achieve the expected flow rate, enhance oil recovery factor and ensure economic efficiency. However, the oil production flow rate is traditionally predicted by theoretical or empirical models. The theoretical model usually gives predicted results with a wide variation of error, this model also requires a lot of input data that might be time-consuming and costly. The empirical models are often limited by the volume of data set used to construct the model, therefore predicted values from the applications of these models in practical condition are not highly accurate. In this research, the authors propose the use of an artificial neural network (ANN) to establish a better relationship between production properties and oil production flow rate and predict oil production flow rate. Using production data of 5 wells which use continuous gas lift method in X oil field, Vietnam, an ANN system was developed by using back-propagation algorithm and tansig function to predict production flow rate from the above data set. This ANN system is called a back-propagation neural network (BPNN). In comparison with the oil production flow rate data collected from these studied continuous gas lift oil wells, the predicted results from the constructed ANN achieved a very high correlation coefficient (98%) and low root mean square error (33.41 bbl/d). Therefore, the developed ANN models can serve as a practical and robust tool for oilfield prediction of production flow rate.