Predictive Modeling of Microemulsion Phase Behaviour and Microstructure Characterisation in the 1-Phase Region

Rout, D. K.; Goyal, Richa; Sinha, Ritesh; Nagarajan, Anantha Venkataraman; Paul, Pintu

doi:10.5772/35539

Cited by 2 publications

References 88 publications

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Advantages and limitations of different methods to determine the optimum formulation in surfactant–oil–water systems: A review

Marquez,

Ontiveros,

Barrios

et al. 2023

J Surfact & Detergents

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The optimum formulation in a surfactant–oil–water (SOW) system is defined as the physicochemical situation at which the surfactant adsorbed at the interface exhibits exactly equal interactions for both oil and water. Identifying the optimum formulation of SOW systems is crucial in various industrial applications, ranging from pharmaceuticals to cosmetics and to petroleum issues like dehydration and enhanced oil recovery. Multiple techniques are available to identify the optimum formulation, often with its own advantages and limitations. In this comprehensive review, we provide an in‐depth analysis of the systematic use of formulation scans to identify the optimum formulation in SOW systems. We critically assess different methods, including conventional ones, such as phase behavior observation, determination of the minimum interfacial tension from equilibrated systems, and the localization of the minimum emulsion stability using formulation scans. We also mention a new promising technique that can be applied in practice, such as oscillating spinning drop interfacial rheology (OSDIR) as well as others that allow an understanding of some structural features of the domains present in the surfactant‐rich phase in SOW systems. Among these methods, dynamic light scattering (DLS), small angle scattering (SAXS and SANS), nuclear magnetic resonance (NMR), X‐ray microcomputed tomography (Micro‐CT), and differential scanning calorimeter (DSC), can be found in the literature. Finally, we discuss potentially unusual behaviors that can appear in complex systems, thus providing guidance on the selection of the most suitable method tailored to the specific application.

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Advantages and limitations of different methods to determine the optimum formulation in surfactant–oil–water systems: A review

Marquez,

Ontiveros,

Barrios

et al. 2023

J Surfact & Detergents

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Digital Oil Model Development and Verification

Hon

Saaid

Chai

et al. 2021

Day 1 Tue, March 23, 2021

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Advances in digital technologies have the potential to enhance model predictive capability and redefine its boundaries at various scale. Digital oil with accurate representation of atomistic components is a powerful tool to analyze both macroscopic properties and microscopic phenomena of crude oil under any thermodynamic conditions. Digital oil model presented in this paper is the key input in molecular chemistry modeling for designing chemical enhanced oil recovery formulation. Hence, it is constructed based on a fit-for purpose strategy focusing in oil components that have large contribution to microemulsion stability. Complete crude oil composition could comprise over 100,000 components. Lengthy simulation time is required to simulate all crude oil components which is impratical, despite the challenges to identify all crude oil components experimentally. Therefore, we established a practical experimental strategy to identify key crude oil components and constructed the digital oil model based on surrogate components. The surrogate components are representative molecules of the volatiles, saturates, aromatics and resins. Two-dimensional digital oil model, with aromaticity on one axis, and the size of the molecules on the other axis was constructed. We developed algorithm to integrate nuclear magnetic resonance response with architecture of the molecular structure. A group contribution method was implemented to ensure reliable representation of the molecular structure. We constructed the digital oil models for a field in Malaysia Basin. We validated the physical properties of the digital oil model with properties measured from experiment, predicted from molecular dynamics simulation and calculated from quantitative property-property relationship method. Good agreement was obtained from the validation, with less than 5% and 13% variance in crude density and Equivalent Alkane Carbon Number respectively, indicating that the molecular characteristic of the digital oil model was captured correctly. We adopted the digital oil model in molecular chemistry modeling to gain insights into microemulsion formation in chemical enhanced oil recovery formulation design. Digital oil is a robust tool to make predictions when information cannot be extracted from experimental data alone. It can be extended for engineering applications involving processing, safety, hazard, and environmental considerations.

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Predictive Modeling of Microemulsion Phase Behaviour and Microstructure Characterisation in the 1-Phase Region

Cited by 2 publications

References 88 publications

Advantages and limitations of different methods to determine the optimum formulation in surfactant–oil–water systems: A review

Advantages and limitations of different methods to determine the optimum formulation in surfactant–oil–water systems: A review

Digital Oil Model Development and Verification

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