Modeling Yemeni Crude Oil Reservoir Fluid Properties Using Different Fuzzy Methods

Abstract

Calculations for the production of petroleum, modeling, and reservoir characterization primarily rely on reservoir fluid parameters including the “bubble point pressure” (Pb), “formation volume factor” (βo), “solution gas oil ratio” (Rs), and viscosity. This paper aims to predict the Yemeni crude oil reservoir fluid parameters using different fuzzy approaches. The fuzzy model was optimized using eight different types of input membership functions, ten cluster radius values, linear and constant output membership function in order to obtain the best fuzzy logic (FL) parameters. Field data was used to build the proposed model, such as temperature and the specific gravity of gas and oil. The data was gathered from a variety of wells in well-known Yemeni reservoirs. Various evolution criteria were employed using statistical error analysis, including an “average absolute percent relative error” (AAPRE), “standard deviation” (SD), and the “correlation coefficient” (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), to assess the effectiveness and correctness of the suggested FL models. The statistical analysis showed that the gaussmf function was the best input membership function, while the linear function was the best output function. The ideal cluster radius for the radius was 0.04. Correlation coefficients of 0.993, 0.995, and 0.990 were obtained by the best fuzzy logic models for “bubble point pressure” (Pb), “formation volume factor” (βo), and “solution gas oil ratio” (Rs), respectively.