A straight line equation is generally used to estimate well inflow performance above bubble-point pressure. However, when the pressure drops below the bubble-point pressure, the trend deviates from that of the simple straight line relationship. Although some analytical methods can accurately represent the horizontal 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 algorithms are increasingly powerful and reliable tools for petroleum engineers to analyze and interpret different areas of oil and gas industry. In this paper, two neuro-fuzzy models, including Local Linear Neuro-Fuzzy Model (LLNFM) and Adaptive Neuro Fuzzy Inference System (ANFIS) have been compared with Multi-Layer Perceptron (MLP) and empirical correlations to predict the inflow performance of horizontal oil wells experiencing two phase flow.

Several reservoir models have been simulated with different bottomhole pressures. The models contained a wide range of absolute and relative permeabilities, PVT data, and horizontal well lengths. The necessary training data have been obtained from 80% of simulation results, covering a wide range of fluid and rock properties. The other 20% are used for error checking and performance testing. The results show that the Local Linear Neuro-Fuzzy Model gives the smallest error for unseen data, when compared to other intelligent models and empirical correlations.

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