No abstract

The importance of miscible displacements in the petroleum industry makes their understanding and quantitative prediction critical in decisions on the applicability of certain recovery techniques. In this study, scaling miscible displacements in porous media was investigated using a general procedure of inspectional analyis. The procedure was used to derive the minimum number of dimensionless scaling groups which govern miscible displacements. It was found that scaling miscible displacements in a two-dimensional, homogeneous, anisotropic vertical cross-section requires the matching of nine dimensionless scaling groups. A numerical sensitivity study of the equations was performed to investigate the effects of some of the scaling groups on the performance of miscible displacements. Through this sensitivity study, it was found that one of the groups is insensitive to the results over all practical values. Hence, the problem can be scaled by only eight dimensionless scaling groups. The prediction of the recovery efficiency for miscible EOR processes can be achieved solely by analyzing these scaling groups. Preliminary results indicate that when the groups are used as inputs to an artificial neural network, the efficiency of the displacement can be accurately predicted.

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.