A document by Saman Aryana. Click on the document to view its contents.

A document by Saman Aryana. Click on the document to view its contents.

CO2-foams show significant potential for improving mobility control in CO2 Enhanced Oil Recovery (EOR) processes and for the geological carbon sequestration. The addition of nanoparticles (NPs), such as Fe3O4, has been shown to enhance the stability of these foams, especially when used in conjunction with surfactants. Apart from their role in stabilization, Fe3O4 NPs possess distinctive magnetic properties, making them useful in various applications. In this study, we explore the novel application of these nanoparticles, initially intended for stabilizing CO2-foams, as tracers for monitoring the migration of subsurface CO2 plumes. This monitoring is envisioned through the detection of small quantities of these particles within subsurface fluid environments. To facilitate this, we use an Induction Heating (IH) technique, involving exposing a solution with small amounts of Fe3O4 NPs to a high-frequency alternating magnetic field, then measuring the resulting temperature changes using an infrared camera variations. The results indicate a direct correlation between the NP concentration and the observed temperature increase in the solution.

Current and emerging approaches to subsurface storage suffer from geographical limitations and in some cases insufficient seal integrity or lateral containment. We propose an innovative containment strategy based on time-dependent yield stress materials, namely Smectite clay suspensions, to address these challenges and make subsurface storage reliable and geographically agnostic. We outline a containment strategy designed to reinforce natural subsurface seals and engineer flow barriers. As a high-risk, high-reward approach, suspension can be injected at its initial low viscosity and elasticity into a porous medium, allowing for easy pumping and targeted delivery, once inside the target zone, it matures into a soft solid with much higher viscosity and elasticity, acting as a flow barrier. We introduce and discuss the exceptional properties of this Smectite suspensions that we believe can revolutionize subsurface containment and storage to move in tandem with the energy transition. There are no adverse effects from higher temperatures on its long-term stability, unlike most polymer aqueous dispersions used in the industry. Moreover, its thixotropic microstructure offers many advantages in operations, such as handling sudden pump shutdowns.

The efficient development of shale gas reservoirs requires an accurate understanding of methane gas transport in the matrix whose pore size is mainly in the nanoscale range. As a result, continuum-based approaches may be inadequate in simulating flow in such systems. Molecular dynamics (MD) simulations are capable of capturing the relevant microscale physics with high fidelity, albeit at a substantial computational cost. This high expense restricts MD simulations to rather small systems and computational domains, which may not be representative of complex hierarchical nature of shale reservoirs. To bridge this gap, we use a particle-based approach, the lattice Boltzmann method (LBM), as a suitable means to capture the physics of transport at microscale and simulate large complex domains. In this work, the multiple-relaxation-time (MRT)-LBM is used to study methane transport in nano-size pores. The adsorption effect and non-ideal gas behavior are incorporated using the pseudopotential model and appropriate force terms. The optimal values of the LB free parameters are determined for a nano-slit pore using reference velocity and density profiles from MD simulations. A preconditioning scheme is proposed to improve the stability of LBM in the presence of force terms. In this scheme, steady-state profiles obtained in the absence of regularization are used as the initial condition for simulation runs that include the regularization step. The results show how roughness adversely affects gas-transport in nanopores. The stability of the proposed framework makes it a potential approach for studying methane transport in more complex nano-porous media and translating transport behavior across scales.

Fluid mixing in permeable media is essential in many practical applications. The mixing process is a consequence of velocity fluctuations owing to geological heterogeneities and mobility contrast of fluids. Heterogeneities in natural rocks are often spatially correlated, and their properties, such as permeability, may be described using fractal distributions. This work models the fractal characteristics of such permeability fields in which the covariance function is expressed as a power-law function. A generalized scaling relation is derived relating various fractal permeability fields using the magnitude of their fluctuations. This relation reveals the self-similar behavior of two-phase flow in such permeable media. To that end, a recently developed, high-resolution numerical simulator is employed to validate the analytically derived scaling relations. Two flow problems are considered in which flow is governed by 1) a linear, and 2) a nonlinear transport equation. Due to the probabilistic representation of the fractal permeability fields, a sensitivity study is conducted for each flow scenario to determine the number of realizations required for statistical convergence. Scaling analysis is performed using ensemble averages of simulated saturation profiles and their mixing lengths. Results support the validity of the developed scaling relation across the range of investigated flow conditions at intermediate times. The dynamics of linear flow in the asymptotic regime is affected by the correlation structure of heterogeneity. In nonlinear flow, scaling behavior appears to be dominated by the degree of nonlinearity.