Abstract:The Inflow Performance relationship (a Cartesian plot of bottom-hole flowing pressure versus surface flow rate) is considered one of the diagnostic tools used by petroleum engineers to evaluate the performance of a flowing well. The plot is used to determine whether any well under consideration is performing as expected or not. If it is not, then remedial action may be necessary. The equation that describes this curve is the Inflow Performance Relationship. This equation can be determined both theoretically and empirically.This study presents both conventional methods and artificial intelligence techniques for predicting inflow performance relationship for a dry gas reservoir. The data used in this study was collected from conventional PVT reports for a Yemeni dry gas reservoir.Statistical analysis was performed to see which of these methods are more reliable and accurate method for predicting the inflow performance relationship for the dry gas reservoir. Pseudo pressure approach is the lowest Average Absolute Relative Error (AARE) of all the three conventional methods with AARE (13.282%). The artificial intelligence techniques provide better estimation of the inflow performance relationship than conventional methods with average absolute relative error 0.029% and 0.0001% for artificial neural network and fuzzy logic respectively.

Keywords: Inflow performance relationship, developed new model, Yemeni dry gas reservoirs, artificial intelligence.

| DOI: 10.17148/IARJSET.2020.71003