Use of Fuzzy Logic for Predicting Two Phase Inflow Performance Relationship of Oil Wells

فاقد چكيده

For pressures above bubble-point pressure, a straight line equation is generally used to estimate the well inflow performance. However, when the pressure drops below the bubble-point pressure, the trend deviates from that of the simple straight line relationship. Although 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 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 vertical oil wells experiencing two phase flow. The necessary training data have been obtained from fourteen out of sixteen different simulated reservoir models, covering a wide range of fluid properties and relative permeabilities. The other two models are used for error checking and performance testing. The results show that the Local Linear Neuro-FuzzyModel gives the smallest error for unseen data, when compared to other intelligent models and empirical correlations.