Journal of Chemical and Petroleum Engineering
Volume:57 Issue: 2, Dec 2023

Due to increasing concerns about global warming regarding CO2 release to the atmosphere, various methods are used to capture CO2, among which chemical absorption via amine mixture solutions is very well developed. A set of 179 data related to CO2 absorption in a mixture, including a physical absorbent (sulfolane) and a chemical absorption (AEEA) in a wide range of temperature, pressure and solvent concentration is used to develop two Artificial Neural Networks (ANN). In Multi-Layer Perceptron (MLP), the Levenberg-Marquardt method is used to train the network. Most important factors such as regression analysis value (R2) of 0.99963, Mean Squared Error (MSE) value of 1.22E-05 and Average Absolute Relative Deviation value (%AARD) of 0.2671 factors reveal that the MLP network has a high capability to predict CO2 loading (αCO2). Also, a Radial Basis Function (RBF) network was developed. RBF network with a spread value of 2.2 and 138 neurons had an outstanding performance and achieved an MSE value of 2.53E-05 along with an R2 value of 0.99993, 11 seconds, and a %AARD value of 0.1460. According to experimental and predicted data, the neural networks are well trained and are able to predict CO2 loading precisely in an economic and optimized way.

Molecular dynamics simulations have been performed in this study to predict the diffusion coefficient of benzene in hexane and vice versa by Materials Studio software. COMPASS force field has been applied to the system for optimization of the structures of benzene and hexane molecules. To model and calculate the van der Waals and electrostatic potential energies, a group-based summation method has been utilized. In order to predict the diffusion coefficient, firstly the simulation time and the force field have been optimized. In all simulations, Ewald and Atom-based summation methods were employed to calculate electrostatic and van der Waals potential energies. The optimized simulation times for the diffusion of benzene in hexane with the mole fraction of 0.2, and the diffusion coefficient of hexane in benzene with the mole fraction of 0.8, have been obtained to be 35 and 25 ps, respectively. In addition, the best force field to predict the diffusion coefficient has been identified to be “Pcff”.

The Study focuses on examining the formation and deposition process of sedimentary formations containing oil and gas in the sedimentary basins on Vietnam's continental shelf. Determining the distribution trend, characteristics, and scale of these sediments is of great importance. By analyzing field samples, lithologic data, references, documents, and conducting geological-geophysical research, the author has investigated the formation process of lower Miocene sediments with oil and gas in the Cuu Long basin. Through the study of identified characteristics, distribution scale, sedimentological features, and seismic data, the author has shed light on the process of formation and the trend of hydrocarbon accumulation in the lower Miocene sedimentary basin of the Cuu Long Basin. The predominant trend of sediment deposition distribution is observed from the continental portion of the Dalat Zone towards the Cuu Long Basin, cutting through it in a west-east direction. Furthermore, the research reveals that the lower Miocene sandstone in the Cuu Long Basin can be divided into two sequences: BI.2 and BI.1, both originating from granitoid sources. These findings also contribute to our understanding of the evolution of magma-sediment processes in the studied area and its surrounding regions. Additionally, tectonic extensions and climate changes, which lead to sea-level rise during the lower Miocene sequence, create favorable conditions for the deposition and accumulation of sedimentary particles.

Limited resources and problems caused by fossil fuels consumption, have led researchers to pay attention to reproducible resources. In this study thermal and catalytic pyrolysis process were used to produce bio-oil from sewage sludge. In thermal pyrolysis, the effect of temperature, the heating rate and gas flow rate was investigated and optimum conditions for production of maximum amount of bio-oil with a production yield of 34.7% were determined equal to temperature 525 °C, heating rate of 20 °C/min and gas flow of 0.5 L/min. To improve the quality of bio-oil and reduce the number of oxygenated compounds, four catalysts HZSM-5 (SAR=40), Co/HZSM-5, Ni/HZSM-5 and Mo/HZSM-5 with weight ratios of 1:5 and 1:10 were used. Bio-oil produced by Mo/HZSM-5 catalyst with weight ratio of 1:5 and with factor groups of alkane/alkenes 27.72%, aromatic compounds 6.25 %, oxygenated compounds 5.82%, phenolic compounds 10.75% and high heat value 39.44 MJ/kg. Although the heating value of bio-oil produced from the catalytic pyrolysis of sewage sludge is lower than gasoline and bio-diesel, it is expected that by improving the quality of bio-oil, it will be used instead of fossil fuel in the future.

Digital rock technology has emerged as a powerful tool for analyzing reservoir rocks in the petroleum industry. Technically, Digital Rock Physics (DRP) is an effective method for determining reservoir rock properties. The article reviews the history of digital rock, from its origins in the study of porous media to its development into a practical tool for the petroleum industry. The features of digital rock are discussed, including the use of X-ray microcomputed tomography and pore-scale modeling, which allow for the analysis of rock samples at the pore-scale. The philosophy and science behind digital rock are explored, emphasizing the importance of understanding the fundamental physics of fluid flow in porous media. The applications of digital rock in the petroleum industry are discussed, including its use in reservoir characterization, fluid flow simulation, and enhanced oil recovery. The benefits and limitations of digital rock are examined, highlighting the need for careful interpretation of results and the importance of complementary laboratory techniques. The role of pore network modeling in digital rock technology is also discussed, which allows for the simulation of fluid flow in porous media at the pore-scale. Finally, the article discusses future directions for digital rock, including the development of new imaging and modeling techniques and the integration of digital rock with other data sources. Overall, digital rock technology, including pore network modeling, is a promising tool for the petroleum industry that has the potential to improve the understanding of reservoir rocks and enhance hydrocarbon recovery.

The increasing pollution levels, rising energy demand, and the inadequacy of conventional fuels have spurred interest in alternative sources of fossil fuel supply and demand. This problem has shifted the focus towards the examination of a promising sustainable alternative for diesel fuels. In this sense, biodiesel can be a more suitable candidate for energy, environmentally harmless, and cost-competitive approach to respond to the energy demand. The application of ultrasonic energy in biodiesel production via transesterification has been endorsed as an efficient approach that improves mass transfer factors resulting in decreased reaction times and potentially lower process expense. This study investigates the advancements in ultrasonic energy for biodiesel generation from various raw materials utilizing different catalysts. A critical assessment of the current approach is furnished, emphasizing the application of ultrasonic irradiation. Besides, in order to better understand ultrasonic energy, each ultrasonic cavitation (UC) and hydrodynamic cavitation (HC) is discussed. Regarding each approach, the fundamental concepts are discussed in detail. Generally, the present study aims at conveying a comprehensive overview of ultrasonic energy usage in transesterification reactions and furnishing an outlook on prospective developments in the technology.

Inclined submerged diffusers that are used to dilute hypersaline and highly contaminated brine, discharged from desalination plants, in receiving marine waters are commonly modeled via semi-empirical, integral, and 3D Computational Fluid Dynamic (CFD) models. The first two models are computationally simple and efficient, but not enough accurate in many cases, and 3D-CFD models which show good agreement with experimental data are time-consuming. To avoid computational costs of 3D models and to present a more precise model than simple ones, a modified 2D-CFD model for stagnant and dynamic ambient is suggested in this study. The results showed that the proposed model can predict the jet behavior in both ambients more accurately than integral models and in shorter computing time than 3D models. The results of this study can be used in order to design environmentally friendly discharge systems by engineers and practitioners for brine or pollutant dilution in the receiving marine waters.

The Heating value of natural gas is used to determine the quality of the gas sample, hence accurate prediction of heating value helps in controlling the issue of underbilling and overbilling between a gas aggregator and an off-taker. Moreover, the heating value of natural gas is not a fixed value and the accuracy of it in real-time is essential. This study was focused on the prediction of the Higher Heating Value (HHV) of natural gas based on percentage gas compositions obtained from Ghana’s offshore oil fields using Artificial Neural Networks (ANN), Adaptive Boost (AdaBoost), Extreme Gradient Boost (XGBoost), Linear Regression (LR). These algorithms were modelled to determine the best predictive model using 2021 sample data on gas specifications. Eighty percent (80%) of the data was used for training and the remaining 20% was used for testing. The performance of each algorithm was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R2 and Adjusted R2. XGBoost performed better than all the other predictive models with an R2 and adjusted R2 of 91.18% and 90.93% respectively and RMSE, MAE, and MAPE of 1.7302, 0.5393 and 0.57% respectively. The incorporation of this method provides a diverse approach to the analysis of the pipeline dynamic results of the heating value of natural gas.

Reservoir modeling and simulation play a pivotal role in the field of reservoir engineering, enabling efficient hydrocarbon recovery and reservoir management. This article provides an overview of the definition, significance, and evolution of reservoir modeling techniques, emphasizing the importance of accurate reservoir characterization. It explores different data acquisition methods, such as core analysis, well logging, seismic data, and production history, highlighting their integration for robust reservoir description. Mathematical modeling techniques for reservoir simulation, including single-phase and multi-phase flow models, along with numerical simulation methods such as finite difference, finite element, and finite volume, are discussed. The article also delves into uncertainty analysis, history matching, and the assimilation of field production data to improve model accuracy. Advanced techniques, emerging trends, and their applications, such as upscaling/downscaling methods, integrated reservoir modeling and optimization approaches, and the use of artificial intelligence and machine learning, are presented. The inclusion of case studies showcases the practical implementation of reservoir modeling and simulation in various areas, such as field development planning, enhanced oil recovery, and reservoir management. Finally, the challenges associated with reservoir modeling and simulation techniques and future perspectives for advancements in the field are addressed.

With the development of the automotive industry, waste tire production has also increased significantly. And their accumulation causes many problems and occupies a large space. One of the most effective solutions is recycling waste tires by devulcanization, which is more economical than re-production. This paper aims to investigate the relationship between microstructure and physio-mechanical properties of devulcanized rubber samples. For this purpose, mechanical tests and identification tests including FTIR (Fourier-transform infrared spectroscopy) and NMR (Nuclear Magnetic Resonance) were performed on the samples. It was determined that; there is an inverse relationship between sol weight fraction and NR/SBR ratio; the lower the ratio, the higher the vinyl bonds, tensile strength, and swelling index of the specimens, and the better the curing. On the other hand, increasing this ratio decreases the percentage of devulcanization. Therefore, the low NR/SBR ratios will increase the process efficiency and improve the mechanical quality of the devulcanized rubbers.

Nowadays, rotating packed beds (RPBs) have been adopted in the many chemical processes such as absorption, desorption, distillation, and etc. Due to the complex structure of RPBs, Computational Fluid Dynamic (CFD) is adopted for analyzing air-water flow in the RPB. In this work, increasing nozzle from 2 to 8 on the behavior of air and water flows was investigated and validated with the experimental data with deviations less than 14%. The obtained results of RPB with packing and baffles demonstrated that increasing nozzle from 2 to 6 increased air velocity vectors. Also, increasing nozzle from 2 to 6 in the RPB with packing uniformed the water velocity on the rotor and housing. In the end, RPB with baffles increased momentum of water velocity vectors and velocity gradient on the rotor and housing. The obtained results showed that the RPB with 6 nozzles have the uniform air flow pattern rather than other nozzle design. Also, in the RPB with baffles; flooding occurred in all sections of the RPB with 8 nozzles. Furthermore, velocity vectors of the outer edge rotor were larger than the inner edge rotor in the RPB with packing and baffles.

In this study, the significance of temperature within the range of 25 to 70 degrees Celsius and the particle size of calcium carbonate at 237 μm, 348.5 μm, and 497.5 μm on the hydrochloric acid reaction rate with calcium carbonate stone was deliberated. The slow reaction rate helps a retarded action, and therefore, HCl penetrates deeper, and the zones farther from the wellbore will be affected by the acidizing. Therefore, the exploration of variables influencing the reaction rate holds considerable significance. The reaction rate is controlled by influential factors such as temperature and particle size, where the effect of these two factors is discussed in detail. According to the empirical findings, the reaction rate exhibits an upward trend as temperature rises and as the size of calcium carbonate particles decreases. It is worth mentioning that the highest and lowest reaction rates were observed at temperatures of 70 and 25 degrees Celsius, and with calcium carbonate particle sizes of 237 and 497.5 micrometers, respectively. Consequently, this research, considering the examined parameters and the obtained results, contributes to a better understanding and more efficient design of the acidizing process.