Prediction of critical gas flow rate for gas wells unloading

The gas well loading phenomenon is considered one of the most serious problems in the gas industry. It occurs as a result of liquid accumulation in the wellbore, when the gas phase does not provide sufficient energy to lift the produced fluids, imposing additional hydrostatic pressure on the reservoir and causes more reduction in the transport energy. Eventually, if the reservoir pressure is low, then the accumulated liquids may completely kill the well. If the reservoir pressure is higher, then liquid slugging or churning may take place and gives more chance for liquid accumulation and the well may die. To solve such a problem, the well may be unloaded mechanically using a pump or by gas lift; or to let the well continuously unloading itself. Analysis of the mechanisms of gas-well load-up indicates that there is a critical gas rate to keep low pressure gas wells unloaded. Predicting that minimum gas flow rate is very crucial. Several researchers have developed various mathematical models to calculate the critical flow rate necessary to keep gas wells unloaded. This paper presents an Artificial Neural Network (ANN) model for predicting the minimum flow rate for continuous removal of liquids from the wellbore. The model is developed using field data from different gas wells. These data were used to train a three-layer backpropagation neural network model. The model was tested against published field data which was not used in the training phase. The results show that the developed model provides better predictions and higher accuracy than the published models. The present model provides prediction of the critical gas flow rates with an absolute average percent error of 4.61 % and a correlation coefficient of 99.11%.