Computational study of the thermophysical properties of graphene oxide/vacuum residue nanofluids for enhanced oil recovery

Abstract

Prior research suggests that the use of nanotechnology may greatly improve the efficiency of enhanced oil recovery methods, especially hot fluid injection. The thermophysical characteristics of the nanofluid may have an enormous effect on how well the injection process works. However, it takes both time and resources to conduct laboratory analyses of the effects of thermophysical characteristics on the effectiveness of nanofluid-based improved oil recovery methods. Computational models can effectively forecast the thermophysical characteristics of nanofluids and how they affect oil recovery efficiency, which helps overcome this difficulty. The current study investigates the flow of vacuum residue (VR) fluid, which generates entropy when suspended graphene oxide (GO) nanoparticles. When mixed convection and variable thermal conductivity are present, a static/moving wedge allows the nanofluid to propagate. The continuity, energy, entropy, and momentum equations form the foundation of the governing model. We use certain similarity variables to simplify the suggested mathematical formulations into forms for nonlinear differential equations (DEs). We show the results of the reduced equations using the Chebyshev collocation method. We present the graphical and numerical results for all the emerging parameters. For enhanced oil recovery applications, the current results are beneficial.