No abstract

In absence of experimental oil fluid samples, it is usually difficult to select the suitable correlation to estimate oil properties. However, the accuracy of these empirical correlations has become insufficient for the best calculation. The main objective of this work is to test the capability of Particle Swarm Optimization with Neural Network (PSONN) and Neuro-Fuzzy (NFuzzy) approaches to predict oil properties with simply and accurately. The proposed approaches are developed based on clustering the oil data into the three groups (light, medium and heavy light oil). Over five hundred of black oil samples were collected from Middle East Field to train the hybrid models whereas additional oil data samples were selected to validate. The developed models used to estimate bubble points pressure (Pb), solution gas oil ratio at and below Pb, undersaturated oil compressibility, saturated formation volume factor, saturated and undersaturated density. The recommended guidelines and optimal configuration of the PSONN and NFuzzy models are developed to estimate any oil property in future. Statistical error analyses show that the proposed models exhibit a robust predictive capability for estimating oil properties. The validation results show the PSONN model achieve the lowest average absolute percent relative error (0.04, 2.89 and 1.0) for estimating formation volume factor, gas oil ratio and oil compressibility respectively whereas the NFuzzy model obtains the best approximation in oil density and bubble point pressure with average absolute percent relative error (0.18 and 0.97) respectively.