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Pressure-Volume-Temperature (PVT) characterization of a crude oil involves establishing its bubble point pressure, which is the pressure at which the first gas bubble forms on a fluid sample while reducing pressure at a stabilized temperature. Although accurate measurement can be made experimentally, such experiments are expensive and time-consuming. Consequently, applying reliable artificial intelligence (AI)/machine learning methods to provide an accurate mathematical prediction of an oil's bubble point pressure from more easily measured characteristics can provide valuable cost and time savings. This paper develops and compares four neurocomputing models applying algorithms consisting of a Multilayer Perceptron (MLP), a Radial Basis Function trained with a Genetic Algorithm (RBF-GA), a Combined Hybrid Particle Swarm Optimization-Adaptive Neuro-Fuzzy Inference System (CHPSO-ANFIS), and Least Squared Support Vector Machine (LSSVM) tuned with a coupled simulated annealing (CSA) optimizer. Based on a comprehensive analysis, although the four proposed models yield acceptable outputs, the CHPSO-ANFIS model has the best performance with the average absolute relative deviation of 0.846, the standard deviation of 0.0126, the root mean square error of 43.21, and the correlation coefficient of 0.9902. These algorithms are deployed for the accurate estimation of the bubble point pressure from the giant Ahvaz oil field (Iran).

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