A New 3D Mathematical Model for Simulating Nanofluid Flooding in a Porous Medium for Enhanced Oil Recovery

Two-phase Darcy's law is a well-known mathematical model used in the petrochemical industry. It predicts the fluid flow in reservoirs and can be used to optimize oil production using recent technology. Indeed, various models have been proposed for predicting oil recovery using injected nanofluids (NFs). Among them, numerical modeling is attracting the attention of scientists and engineers owing to its ability to modify the thermophysical properties of NFs such as density, viscosity, and thermal conductivity. Herein, a new model for simulating NF injection into a 3D porous media for enhanced oil recovery (EOR) is investigated. This model has been developed for its ability to predict oil recovery across a wide range of temperatures and volume fractions (VFs). For the first time, the model can examine the changes and effects of thermophysical properties on the EOR process based on empirical correlations depending on two variables, VF and inlet temperature. The governing equations obtained from Darcy's law, mass conservation, concentration, and energy equations were numerically evaluated using a time-dependent finite-element method. The findings indicated that optimizing the temperature and VF could significantly improve the thermophysical properties of the EOR process. We observed that increasing the inlet temperature (353.15 K) and volume fraction (4%) resulted in better oil displacement, improved sweep efficiency, and enhanced mobility of the NF. The oil recovery decreased when the VF (>4%) and temperature exceeded 353.15 K. Remarkably, the optimal VF and inlet temperature for changing the thermophysical properties increased the oil production by 30%.

Experimental Core Flooding Investigation of New ZnO-γAl2O3 Nanocomposites for Enhanced Oil Recovery in Carbonate Reservoirs

Generally, crude oil production in mature oil reservoirs is difficult. In this regard, some nanoparticles have been used to upgrade injected water into oil reservoirs. These nanoparticles can be used in a variety of injectable waters, including smart water (SMW) with special salinity. This study aims to evaluate the performance of the injection of SMW with ZnO-γAl₂O₃ nanoparticles in enhanced oil recovery (EOR). The performance of SMW with ZnO-γAl₂O₃ nanoparticles in regard to contact angle (CA), interfacial tension (IFT) reduction, and oil production with core flooding tests was investigated. The newly prepared ZnO-γAl₂O₃ structure was characterized by energy dispersive X-ray (EDX), Fourier transform infrared (FT-IR) spectroscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD) analyses in this research. The effects of different concentrations of nanofluids on zeta potential (ZP) and conductivity were investigated. The ZP test confirmed the results of the stability tests of the developed nanofluids in water-based solutions. After the introduction of ZnO-γAl₂O₃ nanoparticles into the formation of brine and SMW solutions, oil-water (O/W) IFT was reduced. Based on the results, the IFT decreased more when nanoparticles and ions were present in the system. The results of the present study showed that at the concentration of SW+300 ppm ZnO-γAl₂O₃, the IFT value reached 11 mN/m from 27.24 mN/m. The results of the CA tests showed that improving the capabilities of salt water in the presence of nanoparticles has resulted in a very effective reduction. Also, in this regard, very hydrophilic wettability was achieved using SMW with stable nanoparticles. Moreover, the results of the present study showed that at the concentration of SMW+300 ppm ZnO-γAl₂O₃ nanoparticles, the CA value reached 31 from 161°. In the end, the solution of SW+300 ppm ZnO-γAl₂O₃ improved the OR by 15 and 24%. This research indicated that it is possible to develop and implement different nanoparticles by combining SMW to manage reservoir rock wettability and maximize OR from carbonate reservoirs. Thus, this combination as an effective agent could significantly increase reservoir sweep efficiency. Thus, as a result, using the established hybrid technique has distinct advantages over using SMW flooding alone.

Pore-scale flow simulation of supercritical CO2 and oil flow for simultaneous CO2 geo-sequestration and enhanced oil recovery

Recently, carbon capture, utilization, and storage (CCUS) with enhanced oil recovery (EOR) have gained a significant traction in an attempt to reduce greenhouse gas emissions. Information on pore-scale CO₂ fluid behavior is vital for efficient geo-sequestration and EOR. This study scrutinizes the behavior of supercritical CO₂ (sc-CO₂) under different reservoir temperature and pressure conditions through computational fluid dynamics (CFD) analysis, applying it to light and heavy crude oil reservoirs. The effects of reservoir pressure (20 MPa and 40 MPa), reservoir temperature (323 K and 353 K), injection velocities (0.005 m/s, 0.001 m/s, and 0.0005 m/s), and in situ oil properties (835.3 kg/m³ and 984 kg/m³) have been considered as control variables. This study couples the Helmholtz free energy equation (equation of state) to consider the changes in physical properties of sc-CO₂ owing to variations in reservoir pressure and temperature conditions. It has been found that the sc-CO₂ sequestration is more efficient in the case of light oil than heavy oil reservoirs. Notably, an increase in temperature and pressure does not affect the trend of sc-CO₂ breakthrough or oil recovery in the case of a reservoir bearing light oil. For heavy oil reservoirs with high pressures, sc-CO₂ sequestration or oil recovery was higher due to the significant increase in density and viscosity of sc-CO₂. Quantitative analysis showed that the stabilizing factor (ε) appreciably varies for light oil at low velocities while higher sensitivity was displayed for heavy oil at high velocities.

A Selection Flowchart for Micromodel Experiments Based on Computational Fluid Dynamic Simulations of Surfactant Flooding in Enhanced Oil Recovery

A selection flowchart that assists, through Computational Fluid Dynamics (CFD) simulations, the design of microfluidic experiments used to distinguish the performance in Chemical Enhanced Oil Recovery (CEOR) of two surfactants with very similar values of interfacial tension (IFT) was proposed and its use demonstrated. The selection flowchart first proposes an experimental design for certain modified variables (X→: porosity, grain shape, the presence of preferential flowing channels, and injection velocity). Experiments are then performed through CFD simulations to obtain a set of response variables (Y→: recovery factor, breakthrough time, the fractal dimension of flow pattern, pressure drop, and entrapment effect). A sensitivity analysis of Y→ regarding the differences in the interfacial tension (IFT) can indicate the CFD experiments that could have more success when distinguishing between two surfactants with similar IFTs (0.037 mN/m and 0.045 mN/m). In the range of modifiable variables evaluated in this study (porosity values of 0.5 and 0.7, circular and irregular grain shape, with and without preferential flowing channel, injection velocities of 10 ft/day and 30 ft/day), the entrapment effect is the response variable that is most affected by changes in IFT. The response of the recovery factor and the breakthrough time was also significant, while the fractal dimension of the flow and the pressure drop had the lowest sensitivity to different IFTs. The experimental conditions that rendered the highest sensitivity to changes in IFT were a low porosity (0.5) and a high injection flow (30 ft/day). The response to the presence of preferential channels and the pore shape was negligible. The approach developed in this research facilitates, through CFD simulations, the study of CEOR processes with microfluidic devices. It reduces the number of experiments and increases the probability of their success.

Insights into the structure-performance relationships of extraction materials in sample preparation for chromatography

Sample preparation is one of the most crucial steps in analytical processes. Commonly used methods, including solid-phase extraction, dispersive solid-phase extraction, dispersive magnetic solid-phase extraction, and solid-phase microextraction, greatly depend on the extraction materials. In recent decades, a vast number of materials have been studied and used in sample preparation for chromatography. Due to the unique structural properties, extraction materials significantly improve the performance of extraction devices. Endowing extraction materials with suitable structural properties can shorten the pretreatment process and improve the extraction efficiency and selectivity. To understand the structure-performance relationships of extraction materials, this review systematically summarizes the structural properties, including the pore size, pore shape, pore volume, accessibility of active sites, specific surface area, functional groups and physicochemical properties. The mechanisms by which the structural properties influence the extraction performance are also elucidated in detail. Finally, three principles for the design and synthesis of extraction materials are summarized. This review can provide systematic guidelines for synthesizing extraction materials and preparing extraction devices.

Insights into the Effects of Pore Size Distribution on the Flowing Behavior of Carbonate Rocks: Linking a Nano-Based Enhanced Oil Recovery Method to Rock Typing

As a fixed reservoir rock property, pore throat size distribution (PSD) is known to affect the distribution of reservoir fluid saturation strongly. This study aims to investigate the relations between the PSD and the oil-water relative permeabilities of reservoir rock with a focus on the efficiency of surfactant-nanofluid flooding as an enhanced oil recovery (EOR) technique. For this purpose, mercury injection capillary pressure (MICP) tests were conducted on two core plugs with similar rock types (in respect to their flow zone index (FZI) values), which were selected among more than 20 core plugs, to examine the effectiveness of a surfactant-nanoparticle EOR method for reducing the amount of oil left behind after secondary core flooding experiments. Thus, interfacial tension (IFT) and contact angle measurements were carried out to determine the optimum concentrations of an anionic surfactant and silica nanoparticles (NPs) for core flooding experiments. Results of relative permeability tests showed that the PSDs could significantly affect the endpoints of the relative permeability curves, and a large amount of unswept oil could be recovered by flooding a mixture of the alpha olefin sulfonate (AOS) surfactant + silica NPs as an EOR solution. Results of core flooding tests indicated that the injection of AOS + NPs solution in tertiary mode could increase the post-water flooding oil recovery by up to 2.5% and 8.6% for the carbonate core plugs with homogeneous and heterogeneous PSDs, respectively.

CFD Investigation of Al2O3 Nanoparticles Effect on Heat Transfer Enhancement of Newtonian and Non-Newtonian Fluids in a Helical Coil

The present study applied computational fluid dynamics (CFD) to investigate the heat transfer of Newtonian (water) and non-Newtonian (0.3 %wt. aqueous solution of carboxymethylcellulose (CMC)) fluids in the presence of Al2O3 nanoparticles. To analyze the heat transfer rate, investigations were performed in a vertical helical coil as essential heat transfer equipment, at different inlet Reynolds numbers. To verify the accuracy of the simulation model, experimental data reported in the literature were employed. Comparisons showed the validity of simulation results. From the results, compared to the aqueous solution of CMC, water had a higher Nusselt number. In addition, it was observed that adding nanoparticles to a base fluid presented different results in which water/Al2O3 nanofluid with nanoparticles’ volume fraction of 5 % was more effective than the same base fluid with a volume fraction of 10 %. In lower ranges of Reynolds number, adding nanoparticles was more effective. For CMC solution (10 %), increasing concentration of nanoparticles caused an increase in the apparent viscosity. Consequently, the Nusselt number was reduced. The findings reveal the important role of fluid type and nanoparticle concentration in the design and development of heat transfer equipment.

Multiphasic Reaction Modeling for Polypropylene Production in a Pilot-Scale Catalytic Reactor

In this study, a novel multiphasic model for the calculation of the polypropylene production in a complicated hydrodynamic and the physiochemical environments has been formulated, confirmed and validated. This is a first research attempt that describes the development of the dual-phasic phenomena, the impact of the optimal process conditions on the production rate of polypropylene and the fluidized bed dynamic details which could be concurrently obtained after solving the model coupled with the CFD (computational fluid dynamics) model, the basic mathematical model and the moment equations. Furthermore, we have established the quantitative relationship between the operational condition and the dynamic gas⁻solid behavior in actual reaction environments. Our results state that the proposed model could be applied for generalizing the production rate of the polymer from a chemical procedure to pilot-scale chemical reaction engineering. However, it was assumed that the solids present in the bubble phase and the reactant gas present in the emulsion phase improved the multiphasic model, thus taking into account that the polymerization took place mutually in the emulsion besides the bubble phase. It was observed that with respect to the experimental extent of the superficial gas velocity and the Ziegler-Natta feed rate, the ratio of the polymer produced as compared to the overall rate of production was approximately in the range of 9%⁻11%. This is a significant amount and it should not be ignored. We also carried out the simulation studies for comparing the data of the CFD-dependent dual-phasic model, the emulsion phase model, the dynamic bubble model and the experimental results. It was noted that the improved dual-phasic model and the CFD model were able to predict more constricted and safer windows at similar conditions as compared to the experimental results. Our work is unique, as the integrated developed model is able to offer clearer ideas related to the dynamic bed parameters for the separate phases and is also capable of computing the chemical reaction rate for every phase in the reaction. Our improved mutiphasic model revealed similar dynamic behaviour as the conventional model in the initial stages of the polymerization reaction; however, it diverged as time progressed.

Developed Hybrid Model for Propylene Polymerisation at Optimum Reaction Conditions

A statistical model combined with CFD (computational fluid dynamic) method was used to explain the detailed phenomena of the process parameters, and a series of experiments were carried out for propylene polymerisation by varying the feed gas composition, reaction initiation temperature, and system pressure, in a fluidised bed catalytic reactor. The propylene polymerisation rate per pass was considered the response to the analysis. Response surface methodology (RSM), with a full factorial central composite experimental design, was applied to develop the model. In this study, analysis of variance (ANOVA) indicated an acceptable value for the coefficient of determination and a suitable estimation of a second-order regression model. For better justification, results were also described through a three-dimensional (3D) response surface and a related two-dimensional (2D) contour plot. These 3D and 2D response analyses provided significant and easy to understand findings on the effect of all the considered process variables on expected findings. To diagnose the model adequacy, the mathematical relationship between the process variables and the extent of polymer conversion was established through the combination of CFD with statistical tools. All the tests showed that the model is an excellent fit with the experimental validation. The maximum extent of polymer conversion per pass was 5.98% at the set time period and with consistent catalyst and co-catalyst feed rates. The optimum conditions for maximum polymerisation was found at reaction temperature (RT) 75 °C, system pressure (SP) 25 bar, and 75% monomer concentration (MC). The hydrogen percentage was kept fixed at all times. The coefficient of correlation for reaction temperature, system pressure, and monomer concentration ratio, was found to be 0.932. Thus, the experimental results and model predicted values were a reliable fit at optimum process conditions. Detailed and adaptable CFD results were capable of giving a clear idea of the bed dynamics at optimum process conditions.