Prediction of cutting inflow performance relationship of a gas field using artificial intelligence techniques

The Inflow Performance relationship is considered one of the diagnostic tools used by Petroleum engineers to evaluate the performance of a flowing well. An accurate prediction of well IPR is very important to determine the optimum production scheme, design production equipment, and artificial lift systems. For these reasons, there is a need for a quick and reliable method for predicting the well IPR in gas reservoirs. This study presents back propagation network (BPN) and fuzzy logic (FL) techniques for predicting IPR for a gas reservoir. These models involved 489 data points from published literature papers and conventional PVT reports. Statistical analysis was performed to see which of these methods are more reliable and accurate method for predicting the inflow performance relationship for the gas reservoir. The FL model outperformed the artificial neural network (ANN) model with least average absolute error, least standard deviation and highest correlation coefficient. The proposed fuzzy logic well inflow performance relationship model achieved an average absolute error of 4.303%%, standard deviation of 18.891% and the correlation coefficient of 0.995. The developed technique will help the production and reservoir engineers to better manage the production operation without the need for any additional equipment. This technique will reduce the overall cost of the operation and increase the revenue.