Transport of Non-Newtonian Suspensions of Highly Concentrated Micro- And Nanoscale Iron Particles in Porous Media: A Modeling Approach

The use of zerovalent iron micro- and nanoparticles (MZVI and NZVI) for groundwater remediation is hindered by colloidal instability, causing aggregation (for NZVI) and sedimentation (for MZVI) of the particles. Transportability of MZVI and NZVI in porous media was previously shown to be significantly increased if viscous shear-thinning fluids (xanthan gum solutions) are used as carrier fluids. In this work, a novel modeling approach is proposed and applied for the simulation of 1D flow and transport of highly concentrated (20 g/L) non- Newtonian suspensions of MZVI and NZVI, amended with xanthan gum (3 g/L). The coupled model is able to simulate the flow of a shear thinning fluid including the variable apparent viscosity arising from changes in xanthan and suspended iron particle concentrations. The transport of iron particles is modeled using a dual-site approach accounting for straining and physicochemical deposition/release phenomena. A general formulation for reversible deposition is herein proposed, that includes all commonly applied dynamics (linear attachment, blocking, ripening). Clogging of the porous medium due to deposition of iron particles is modeled by tying porosity and permeability to deposited iron particles. The numerical model proved to adequately fit the transport tests conducted using both MZVI and NZVI and can develop into a powerful tool for the design and the implementation of full scale zerovalent iron application

Modeling of Magnetic Nanoparticles Transport in Shale Reservoirs

Currently, the application of nanoparticles has attracted much attention due to the potential of nanotechnology to lead to evolutionary changes in the petroleum industry. The literature contains numerous references to the possible use of this technology for enhanced oil recovery, nano-scale sensors and subsurface mapping. Little work has been conducted to establish numerical models to investigate nanoparticle transport in reservoirs, and particularly much less for shale reservoirs. Unlike conventional reservoirs, shale formations are usually made up of four pores systems: inorganic matter, organic matter dominated by hydrocarbon wettability, natural fractures and hydraulic fractures. Concurrently, hydraulic fractures and the associated stimulated reservoir volume (SRV) from induced fractures play a critical role in significantly increasing well productivity. In this project, a mathematical model for simulating nanoparticle transport in shale reservoirs was developed. The simulator includes contributions from Darcy flow, Brownian diffusion, gas diffusion and desorption, slippage flow, and capillary effects based on the extremely low permeability and micro- to nano-scale of the pores. Moreover, these diverse mechanisms are separately applied to different portions of the reservoir due to the variation in media properties. Applications of the model include numerical examples from two-dimensional micro models to macro models, both with organic matter randomly distributed within the inorganic matrix. The effects of varying water saturation, grid pressure, and mass concentration of nanoparticles are shown graphically in these numerical examples. The main conclusion from these models is that, as expected, nanoparticles can only easily flow along with the aqueous phase into the fractures, but their transport into the shale matrix is quite limited, with little transport shown into the organic matter. In addition, based on the magnetic properties of synthesized magnetic carbon-coated iron-oxide nanoparticles, the distribution of the volumetric magnetic susceptibility and the magnetization of reservoir including SRV are simulated and displayed in the numerical cases with and without magnetic nanoparticles. The numerical results demonstrate that magnetic nanoparticles can effectively increase the magnetic susceptibility and the magnetization of reservoir thus producing enhanced signals from well logging devices such as NMR and leading to improved reservoir and fracture characterization. This simulator can provide the benefits of both numerically simulating the transport and distribution of nanoparticles in hydraulically fractured shale formations and supplying helpful guidelines of nanoparticles injection plans to enhance well logging signals. Furthermore, this model can also allow us to mimic the tracer transport flow in unconventional reservoirs

Influence of Stefan blowing on nanofluid flow submerged in microorganisms with leading edge accretion or ablation

The unsteady forced convective boundary layer flow of viscous incompressible fluid containing both nanoparticles and gyrotactic microorganisms, from a flat surface with leading edge accretion (or ablation), is investigated theoretically. Utilizing appropriate similarity transformations for the velocity, temperature, nanoparticle volume fraction and motile microorganism density, the governing conservation equations are rendered into a system of coupled, nonlinear, similarity ordinary differential equations. These equations, subjected to imposed boundary conditions, are solved numerically using the Runge-Kutta-Fehlberg fourth-fifth order numerical method in the MAPLE symbolic software. Good agreement between our computations and previous solutions is achieved. The effect of selected parameters on flow velocity, temperature, nano-particle volume fraction (concentration) and motile microorganism density function is investigated. Furthermore, tabular solutions are included for skin friction, wall heat transfer rate, nano-particle mass transfer rate and microorganism transfer rate. Applications of the study arise in advanced micro-flow devices to assess nanoparticle toxicity

A Literature Review and Transport Modelling of Nanoparticles for Enhanced Oil Recovery

Master's thesis in Petroleum engineeringNanotechnology has been envisioned to transform every sector of industries, particularly in the petroleum industry. Numerous researches, especially on nano-EOR, have been done in the past few years and shown promising results for improving oil recovery. Injected nanoparticles (NPs) are believed to be able to form adsorption layers on the top of grain surface. The adsorptions layers then alter the wettability of the rock and reduce the interfacial tension. Due to the importance of the adsorption, numerous theoretical studies were performed to simulate the transport behavior of NPs in the porous media. The purpose of this thesis is to i) review the state-of-the-art progress of nanoparticles application in the petroleum industry especially in EOR, and ii) simulate the transport and adsorption of nanoparticles in the porous media. Literatures show that various types of nanoparticles can improve oil recovery through several mechanisms such as wettability alteration, interfacial tension reduction, disjoining pressure and mobility control. Parameters such as salinity, temperature, size, and concentration are substantial for nano-EOR. Several experiments indicate that NPs can improve the oil recovery significantly up to 20% after the primary recovery period. Classical Advection-Dispersion Equation (ADE) is commonly used to simulate particles flow in the porous media, but it fails to simulate NPs flow due to the adsorption that occurs. The colloidal filtration theory (CFT) is used in the study to accommodate the adsorption. Several modifications on CFT, such as dual site model (ISTM), increase the number of unknown variables that reduce the efficiency and the accuracy of the model. Therefore, a simple modified linear adsorption model (ML) is proposed by the author, followed by parameter sensitivity study to reduce the unknown parameters and understand each parameter affecting on the model. The simulation result indicates that CFT model is unable to predict the effluent history data. Differently, ML model demonstrates that it can predict the effluent history quite well. The comparison with ISTM indicates that both can simulate the behavior of NPs, and our ML model gives slightly better result than ISTM model. Therefore, the transport and adsorption of NPs can be predicted by the simple linear adsorption model

Finite element simulation of twist forming process to study twist springback pattern

Springback is one of the most common defects found in the metal forming of automotive parts. There are three conditions which can be considered as springback i.e. flange angle change, sidewall curl and twist springback and among them, twist springback is the most complicated problem. This study will focuses on the development of finite element simulation model of the twist forming process. The main aim of this project is to investigate the parameters that may affect the twist springback. Few parameters including twist angle, hardening constant and thickness are explored using finite element (FE) software ANSYS Workbench (16.0). The rectangular mild strips are used to form the twist forming. The standard material properties and stress-strain curve of mild steel had been used to get the springback prediction. The results of springback were measured by the difference of the bending angles before and after unloading process. The results were then be validated with the research made of Dwivedi et al., (2002). The results show that the springback angle reduces as the thickness of strips are increased and also as the angle of twist increases

Transport and Retention of High Concentrated Nano-Fe/Cu Particles Through Highly Flow-Rated Packed Sand Column

The design of an efficient field-scale remediation based on the use of nanoscale zero valent iron (NZVI) requires an accurate assessment of the mobility of such particles in saturated porous media, both during injection in the subsurface (short-term mobility) and later (long-term mobility). In this study, the mobility of highly concentrated dispersions of bimetallic Fe/Cu nanoparticles (d50= 70±5 nm) in sand-packed columns (0.5 m length and 0.025 m inner diameter) was studied. In particular, the influence of flow rate (V = 5×10-4, 1×10-3, 2×10-3 m/s) and injected particle concentrations (2, 5, 8, 12 g/l) was addressed. Breakthrough curves and water pressure drop along the column, averaged effective porosity and final distribution of retained particles along the column were measured. Experimental results evidenced a good mobility of the Fe/Cu particles, with significant breakthrough in all explored experimental conditions of flow rate and C0, without requiring the addition of any stabilizing agent. Clogging phenomenon of the column and also the pore pressure variation during injection period are strongly affected by injected concentration. Clogging due to deposition of particles following a ripening dynamics was observed in particular for C0= 8 and 12 g/l. The experimental data were 23 modeled using the E-MNM1D software. The study has implications for field injection of bimetallic nanoparticles, suggesting that particular care is to be devoted when selecting injection concentration, to avoid porous medium clogging and control the radius of influenc

An Electromagnetics Water Flooding System With Nanofluid For EOR

The major challenge for oil industry is to increase the recovery of oil from the reservoir. EOR by nanofluids induction has been used in water flooding process. This work deals with a new electromagnetics water flooding system using nanofluid for EOR. A simulation on the density of state (DOS) and band structure of zinc oxide (ZnO) and iron oxide (Fe2O3) was carried out; it was observed that the band gap value for ZnO is 0.808ev and for Fe2O3 is 0.201ev. The percentage difference between the band gap of ZnO and Fe2O3 is 301%. For ZnO, Zn 4s state contributes to conduction band and O 2p state contributes to valence band. For Fe2O3 valence band is a mixture of O 2p state and the majority is Fe 3d state, while the conduction band consists of Fe 3d state. As Fe2O3 has lowest band gap, its dielectric constant is greater than ZnO which has the highest band gap, thus it has the lowest dielectric constan

Inversion algorithm of fiber bragg grating for nanofluid flooding monitoring

In the current study, we developed an adaptive algorithm that can predict oil mobilization in a porous medium on the basis of optical data. Associated mechanisms based on tuning the electromagnetic response of magnetic and dielectric nanoparticles are also discussed. This technique is a promising method in rational magnetophoresis toward fluid mobility via fiber Bragg grating (FBG). The obtained wavelength shift due to Fe3O4 injection was 75% higher than that of dielectric materials. This use of FBG magneto-optic sensors could be a remarkable breakthrough for fluid-flow tracking in oil reservoirs. Our computational algorithm, based on piecewise linear polynomials, was evaluated with an analytical technique for homogeneous cases and achieved 99.45% accuracy. Theoretical values obtained via coupled-mode theory agreed with our FBG experiment data of at a level of 95.23% accuracy