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Asphaltene precipitation is considered a precursor of the plugging of oil wells and subsurface equipment and is a topic of continuous interest among companies and academic institutions. Numerous models to predict asphaltene precipitation at reservoir conditions have emerged over the years, and some have been dropped for several reasons. One particular case is the utilization of cubic equations of state such as Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK), which although are relatively simple to code and utilize, have not been as effective in predicting asphaltene precipitation as compared to other models such as the perturbed chain version of the statistical associating fluid theory equation of state (PC-SAFT EOS). However, we have found that after improving the crude oil characterization procedure to obtain a proper set of simulation parameters from the available experimental data, the cubic equation of state can show excellent predictive capabilities in modeling asphaltene onset pressure under gas injection. In this work, we develop a characterization methodology based on the contents of Saturates–Aromatics–Resins–Asphaltenes (SARA) that can be used with PR EOS. Several case studies with published data from six crude oils are conducted to assess the predictive capability of the new approach in modeling asphaltene onset pressure under gas injection. Comparisons are made with PC-SAFT EOS to highlight the advantages and disadvantages of each model. Also, the modeling approach is tested against high-pressure and high-temperature data from four wells from the Middle East that have not been previously published in the literature. The results indicate that PR EOS yields results that are at least as good as those obtained from PC-SAFT in predicting the onset of asphaltene precipitation in crude oil under various amounts and types of gas injection.

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The viscosity of crude oils and their blends is a key parameter for studying hydrocarbon flow in reservoirs with gas injection and well analyzing the performance during gas lift. Even though several models have been developed to predict the viscosity of live crude oils, no study investigated the viscosity modeling of crude oils under gas injection. In this study, the one-parameter friction theory framework is combined with three characterization methods that were previously explored to model the viscosity of live oils (Khemka et al., Fuel, 2021, 283, 118926), and their predictive capabilities are further investigated for modeling the viscosity of blends of crude oil with five different gases. The performance of the recently developed SARA-based characterization using the Peng−Robinson (PR) equation of state (EoS) is compared with the SARA-based method using the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) EoS and the single carbon number (SCN) method using the PR EoS by testing against 392 experimental viscosity data points from 23 different blends. The models' strong predictive capabilities are demonstrated by the fact that while only live oil data at saturation is required to fit simulation parameters, each model predicts the viscosity of various blends in the single-phase region with only 7% average error, which is satisfactory for practical applications. Even in the two-phase region, the predictions are within the experimental uncertainty; however, the SARA-based methods deliver slightly improved viscosity predictions even though they have a lower number of characterized components than the SCN method. Additionally, despite using the relatively simpler PR EoS with the SARA-based method, the viscosity predictions are at least as good as the predictions obtained using the highly advanced PC-SAFT EoS.

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