Abstract
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.
