New pragmatic strategies for optimizing Kihara potential parameters used in van der Waals-Platteeuw hydrate model

Gas Hydrate in Oil-Dominant Systems: A Review

“…According to previous studies by Avlonitis and Sergeeva et al, , most available hydrate dissociation studies are restricted to low- and moderate-pressure situations (e.g., less than 30 MPa). Typically, these data are used to tune the Kihara potential variables of hydrate formers.…”

“…Typically, these data are used to tune the Kihara potential variables of hydrate formers. However, using these variables to predict the hydrate stability zones of oil systems at high pressures, significantly above the system’s bubble point, may result in a large deviation …”

“…It is important to notice that for a given hydrate the absolute hydrate volume would be higher at higher liquid loading, showing that more hydrates could be transported without a rise in the pump ΔP. 24 According to previous studies by Avlonitis and Sergeeva et al, 24,25 most available hydrate dissociation studies are restricted to low-and moderate-pressure situations (e.g., less than 30 MPa). Typically, these data are used to tune the Kihara potential variables of hydrate formers.…”

“…However, using these variables to predict the hydrate stability zones of oil systems at high pressures, significantly above the system's bubble point, may result in a large deviation. 25 2.2. Effect of Wax.…”

Proposing Crystallographic Theory to Deparameterize Hydrate Phase Description: Physical Insights and Validation

“…Here, our approach uses the state correlations of Peyrovedin et al, along with those of Tee et al whenever they are available. For the individual pure guests, Table shows that our formulation secures a competitive performance with the widely used thermodynamic models. ,,,, Further, we categorize the pure guests into three different families, namely alkane (CH 4 , C 2 H 6 , and C 3 H 8 ), sour gas (CO 2 and H 2 S) , and refrigerant (R22, R134a, and R152a), for which, we evaluate our model with accommodating the correlations of Peyrovedin et al in terms of average % AARD-P. For alkane hydrates, our model provides a 3.56% error [5.01% error with the correlations of Tee et al], whereas an error of 5.93% is obtained by the model of Sloan and Koh and 5.31% by that of Chen and Li through data fittings. The ab initio-assisted approach of Klauda and Sandler, with its pure system-specific parameters and large computational load, secures a lowest error of 3.3%.…”