We have developed a multi-scaled coarse-grained molecular modelling framework to compute the optimal salinity at which a microemulsion forms in an oil-brine-surfactant system. The optimal salinity plays an important role in determining the surfactant mixture used in Alkali-Surfactant-Polymer floods for Enhanced Oil Recovery (EOR). The framework uses Dissipative Particle Dynamics (DPD) to study phenomena at the oil-water interface, which are sensitive to the chemical compositions of the crude, brine and surfactant cocktail.
The DPD-based coarse-grained formalism, called the Method of Moments, predicts trends in EOR behaviour. It addresses the question: Which way will a system swing if certain experimental handles are varied? The experimental handles primarily concern the choice of surfactant – chemical structure, topology – and nature of the oil. The utility of such a tool lies in its ability to scan through a list of molecules – e.g., surfactants, crude components – and rank them according to the system’s sensitivity to their presence. At this stage, the crude oil is typically represented by a mixture of linear alkane (dodecane) and a surface-active component. Our library of such surface-active crude components contains 3 members at the moment: nonylphenol, butylcarbazole and naphthenic acid.
The molecular simulations were validated against phase behaviour experiments that studied the effect of surface-active crude components on the optimal salinities of dodecane-brine-surfactant systems. Phase behaviour tests were conducted separately for each crude component. We observed that the optimal microemulsion formed at a lower salinity when the crude component was either nonylphenol or naphthenic acid. The addition of butylcarbazole did not alter the optimal salinity at all. The optimal salinity decreased by ~57% in the presence of 2.04 mol% of nonylphenol and 2.45 mol% of naphthenic acid in separate experiments. This shows that nonylphenol is a more effective surfactant than the acid, since a smaller quantity achieves a comparable dip in the optimal salinity. Our Method of Moments simulations reproduced these trends in the optimal salinity for all three crude components. We now have a coarse-grained molecular model that can handle simple oils doped with a single surface-active crude component. The next step is to (1) broaden the library of crude components, and (2) extend the framework to mixtures of molecules to predict shifts in the optimal salinity.
A molecular simulation technique like DPD relies on forcefields that characterise the interaction of different molecules with each other. It is therefore important to parametrise the interactions of the surface-active species with water and oil to mimic optimal salinities from phase behaviour experiments. The novelty of our method lies in developing these parameters starting from atomistic structures of the interacting species and predicting trends in a macroscopically observable quantity – the optimal salinity – that is crucial in implementing EOR in diverse fields.
See more of this Group/Topical: Computational Molecular Science and Engineering Forum