Modeling of Multiphase Behavior for Water/n-Alkane Mixtures by Use of the Peng-Robinson EOS

摘要

Experimental results in the literature show that the water solubility in the oleic (L) phase can be high at reservoir conditions in thermal oil recovery processes; e.g., 24 mol% in the water/n-eicosane binary system at 41 bars and 523 K. It becomes even more significant as the L phase becomes more aromatic, which is the case with heavy oil and bitumen. Efficient and accurate representation of multiphase behavior, which consists of the L, vapor (V), and aqueous (W) phases, is crucial in reliable numerical simulation of steam injection processes. This research presents multiphase behavior predictions for water/n-alkane mixtures by use of the Peng-Robinson equation of state (PR EOS) with the van der Waals mixing rules. Binary interaction parameters (BIPs) are first optimized for water with n-alkanes in terms of three- phase curves including upper critical endpoints (UCEPs), where the V phase and the less dense liquid phase merge in the presence of the denser liquid phase. A new correlation is then developed on the basis of the optimized BIP values. Thermodynamic predictions from the PR EOS with the new BIP correlation are given for various mixtures and compared with experimental data available in the literature. Results show that the PR EOS with the BIP correlation yields reasonable predictions for multiphase behavior of water/n-alkane mixtures. It gives the transition of binary phase behavior between types IIIa and IIIb that is consistent with experimental results. It also can reproduce asymptotic behavior of three-phase curves and the water solubilities in the L phase (x_(wL)) that has been observed as n-alkane becomes heavier. When applied to water-containing reservoir fluids, the PR EOS with the BIP correlation results in a systematic underprediction of x_(wL). This is expected considering that reservoir oil consists of various types of hydrocarbons and that the affinity towards water is lowest for n-alkanes and highest for aromatics. Accurate x_(wL) predictions can be obtained by systematically reducing the BIP values from the correlation. The correlation developed may serve as the upper limit of BIPs for water with pseudo components that are required for characterizing water-containing reservoir fluids.