Phase behavior and thermodynamics of petroleum reservoir fluids

Due to stringent environmental norms, a lot of research is going on to reduce exhaust emissions coming out from diesel engines. Diesel microemulsion is one of those alternate techniques. In fuel microemulsion, microexplosion triggers complete combustion of all the components and thus, high reduction in hazardous gases from diesel engines. The main focus of this research is to formulate and characterize diesel-based reverse micellar microemulsion. In a blend of two non-ionic surfactants, hydrophilic surfactant dominates the effect of lipophilic surfactant. The interfacial composition of fuel microemulsion was also investigated by Schulman’s method of co-surfactant titration. During the investigation of the effect of chain length of co-surfactants, it was found that the efficiency of microemulsion increases with increasing chain length of co-surfactants. The chain structure of surfactant and co-surfactant also plays a paramount role in microemulsion formulation. The fuel properties like viscosity, cloud and pour points also get affected by chain length and structure of cosurfactants.

Deposition of complex and heavy organic compounds which exist in petroleum crude can cause a number of severe problems. To prevent deposition inside the reservior, in the well head, and inside the transmission lines it is necessary to be able to predict the onset and amount of deposition due to various factors. In the present paper the mechanism of organic deposition is modeled based on statistical mechanics of polydisperse polymer solutions joined with the kinetic theory of aggregation and its predictive capability is discussed. Utilization of statistical mechanics of polydisperse polymer solutions joined with kinetic theory of aggregation enables us to develop a realistic model which is able to predict, both, reversible and irreversible heavy organic depositions. The present model is capable of describing several reversible and irreversible situations, such as the phenomena of organic deposition, growing mechanism of heavy organic aggregates, the size distributions of precipitated organics, and the solubility of heavy organics in a crude oil under the influence of· miscible solvents. As an example the present model is applied for heavy organics deposition prediction of a particular crude oil for which experimental multi-phase behavior data are available. It is shown that the prediction results of the present model are in very good agreement with the experimental data.

A thermodynamic model for the prediction of CO2 hydrate phase stability conditions in the presence of
pure and mixed salts solutions and various ionic liquids (ILs) is developed. In the proposed model van der
Waals and Platteeuw model is used to compute the hydrate phase, Peng–Robinson equation of state
(PR-EoS) for the gas phase and the Pitzer–Mayorga–Zavitsas-Hydration model is employed to calculate
the water activity in the liquid water phase. This model is an extension of the model developed by Tumba
et al. (2011) for the prediction of methane and CO2 hydrate phase stability conditions in the presence of
tributylmethylphosphonium methylsulfate IL solution. Shabani et al. (2011) mixing rule is modified by
incorporating the water–inhibitor (salt/IL) interaction parameter to calculate the water activity in mixed
salt solutions. The model predictions are also calculated using the Pitzer–Mayorga model separately and
compared with predictions of the developed model. The model predictions are compared with
experimental results on the phase stability of CO2 hydrate in the presence of ILs, pure and mixed salts
as reported in literatures. The ILs are chosen from imidazolium cationic family with various anion groups
such as bromide (Br), tetrafluoroborate (BF4), trifluoromethanesulfonate (TfO), and nitrate (NO3) and the
common salts such as NaCl, KCl and CaCl2. Good agreement between the developed model predictions
and the literature data is observed. The overall average absolute deviation (AARD%) with Pitzer–Mayorga–
Zavitsas-Hydration model is observed to be within ±1.36% while Pitzer–Mayorga model accuracy
were about ±1.44 %. Further, the model is extended to calculate the inhibition effect of selected inhibitors
(ILs and salts) on CO2 hydrate formation.