Zulkifli Merican Aljunid Merican

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%.

Cu/ZnO catalysts were synthesized via an impregnation method on an Al₂O₃-ZrO₂ support and modified by the addition of manganese and niobium as promoters. The effect of the selected promoters on the physicochemical properties and performance toward the hydrogenation of CO₂ to methanol are presented in this paper. The Mn and Nb promoters improved the reducibility of the catalyst as evidenced by the shifting of the H₂-TPR peaks from 315 °C for the un-promoted catalyst to 284 °C for the Mn- and Nb-promoted catalyst. The catalytic performance in a CO₂ hydrogenation reaction was evaluated in a fixed-bed reactor system at 22.5 bar and 250 °C for 5 h. Amongst the catalysts investigated, the catalyst with equal ratio of Mn and Nb promoters exhibited the smallest particle size of 6.7 nm and highest amount of medium-strength basic sites (87 µmol/g), which resulted in the highest CO₂ conversion (15.9%) and methanol selectivity (68.8%).

Accurately measuring wettability is of the utmost importance because it influences several reservoir parameters while also impacting reservoir potential, recovery, development, and management plan. As such, this study proposes a new formulated mathematical model based on the correlation between the Amott-USBM wettability measurement and field NMR T₂LM log. The exponential relationship based on the existence of immiscible fluids in the pore space had a correlation coefficient of 0.95. Earlier studies on laboratory core wettability measurements using T₂ distribution as a function of increasing water saturation were modified to include T₂LM field data. Based on the trends observed, water-wet and oil-wet conditions were qualitatively identified. Using the mean T₂LM for the intervals of interest and the formulated mathematical formula, the various wetting conditions in existence were quantitatively measured. Results of this agreed with the various core wettability measurements used to develop the mathematical equation. The results expressed the validity of the mathematical equation to characterise wettability at the field scale. With the cost of running NMR logs not favourable, and hence not always run, a deep ensemble super learner was employed to establish a relationship between NMR T₂LM and wireline logs. This model is based on the architecture of a deep learning model and the theoretical background of ensemble models due to their reported superiority. The super learner was developed using nine ensemble models as base learners. The performance of nine ensemble models was compared to the deep ensemble super learner. Based on the RMSE, R², MAE, MAPD and MPD the deep ensemble super learner greatly outperformed the base learners. This indicates that the deep ensemble super learner can be used to predict NMR T₂LM in the field. By applying the methodology and mathematical formula proposed in this study, the wettability of reservoirs can be accurately characterised as illustrated in the field deployment.

The level of carbon dioxide in the atmosphere is growing rapidly due to fossil fuel combustion processes, heavy oil, coal, oil shelter, and exhausts from automobiles for energy generation, which lead to depletion of the ozone layer and consequently result in global warming. The realization of a carbon-neutral environment is the main focus of science and academic researchers of today. Several processes were employed to minimize carbon dioxide in the air, some of which include the utilization of non-fossil sources of energy like solar, nuclear, and biomass-based fuels. Consequently, these sources were reported to have a relatively high cost of production and maintenance. The applications of both homogeneous and heterogeneous processes in carbon capture and storage were investigated in recent years and the focus now is on the conversion of CO₂ into useful chemicals and compounds. It was established that CO₂ can undergo cycloaddition reaction with epoxides under the influence of special catalysts to give cyclic carbonates, which can be used as value-added chemicals at a different level of pharmaceutical and industrial applications. Among the various catalysts studied for this reaction, metal-organic frameworks are now on the frontline as a potential catalyst due to their special features and easy synthesis. Several metal-organic framework (MOF)-based catalysts were studied for their application in transforming CO₂ to organic carbonates using epoxides. Here, we report some recent studies of porous MOF materials and an in-depth discussion of two repeatedly used metal-organic frameworks as a catalyst in the conversion of CO₂ to organic carbonates.

The problem of global warming is one of the major concerns in the world today as there has been continuous rise in temperature resulting from increase in the emission of anthropogenic carbon dioxide (CO₂). Recently, carbon dioxide is considered as an abundant C1 feed-stock for organic transformations, due to its free availability, non-toxicity, and simplicity in handling. The synthesis of metal-organic framework/polyoxometalates supported composites (POM@MOF), were prepared by incorporation of Keggin type-polyoxometalate groups via impregnation method. The as-synthesized composites were characterized by powder X-ray diffraction (PXRD), fourier-transform infrared spectroscopy (FT-IR), and field emission scanning electron microscope (FESEM), which confirm the presence of the polyoxometalates group after formation of the composites. The composites were employed as catalyst for the conversion of CO₂ and epichlorohydrin into cyclic chloropropylene carbonate. The reaction was carried out under mild condition of atmospheric pressure in a schlenk tube without addition of any co-catalyst or solvent and the yield have shown progressive increase over time as indicated by GC results from 30% at 6 h duration to 87% over a period of 48 h of continuous siring.

Thermal conductivity enhancement of multi-walled carbon nanotubes (MWCNTs) based nanofluids received the attention of many researchers due to its remarkable thermal properties. However, the synthesis of surfactant-free multi-walled carbon nanotubes based nanofluids with higher dispersion stability to maintain a trade-off between its dispersion stability and thermophysical properties is still a challenge. In this research, a surfactant-free multi-walled carbon nanotube and Jatropha seed oil-based nano-dispersion are prepared via one-step method and subjected to experimental determination of thermal conductivity within the temperature range of (25 °C - 65 °C) and nanoparticle volume fraction range of (0.2 wt.% - 0.8 wt.%). The prepared nanofluid found to be highly stable for long time and its thermal conductivity enhancement was found to be 6.76%.