Use of Genetic Programming for Predicting Two Phase Inflow performance relationship of oil wells

for pressured above bubble -point pressure , a straight line equation is generally used to estimate the well inflow performance . However when the pressure drop below the bubble -point pressure , the trend deviates from that of the simple straight line relationship.althought analytical methods can accurately represent the well IPR behavior above bubble point pressure , only empirical correlations are available for IPR modeling of two phase reservoirs and hence some deviations from actual data are often observed. artificial intelligence techniques such as neural networks, fuzzy logic , and genetic programming are increasingly powerful and reliable tools for petroleum engineers to analyze and interpret different areas of oil and gas industry . genetic programming is one of the computer algorithms in the family of evolutionary -computational methods which operates by codifying the solution of the problem as a population of LISP trees. this type of algorithm provides a diagnosis output in the form of a decision tree with given functions and data , which have been shown to provide reliable solutions to complex optimization problems. in this paper , a genetic programming model has been compared with multi -layer perceptron MLP and empirical correlations to predict the in flow performance of vertical oil wells experiencing two phase flow.

کلیدواژه ها:

genetic programming ، artificial intelligence ، inflow performance relationship ، multi -layer perceptron.