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This project investigated the impact of low-temperature, in-situ synthesis of cerium oxide (CeO2) nanoparticles on various aspects of oil recovery mechanisms, including changes in oil viscosity, alterations in reservoir rock wettability, and the resulting oil recovery factor. The nanoparticles were synthesized using a microemulsion procedure and subjected to various characterization analyses. Subsequently, these synthesized nanoparticles were prepared and injected into a glass micromodel, both in-situ and ex-situ, to evaluate their effectiveness. The study also examined the movement of the injected fluid within the porous media. The results revealed that the synthesized CeO2 nanoparticles exhibited a remarkable capability at low temperatures to reduce crude oil viscosity by 28% and to lighten the oil. Furthermore, the addition of CeO2 nanoparticles to the base fluid (water) led to a shift in the wettability of the porous medium, resulting in a significant reduction in the oil drop angle from 140° to 20°. Even a minimal presence of CeO2 nanoparticles (0.1 wt%) in water increased the oil production factor from 29 to 42%. This enhancement became even more pronounced at a concentration of 0.5 wt%, where the oil production factor reached 56%. Finally, it was found that the in-situ injection, involving the direct synthesis of CeO2 nanoparticles within the reservoir using precursor salts solution and reservoir energy, led to an 11% enhancement in oil production efficiency compared to the ex-situ injection scenario, where the nanofluid is prepared outside the reservoir and then injected into it. [ABSTRACT FROM AUTHOR]

Visualizing fluid flow in porous media can provide a better understanding of transport phenomena at the pore scale. In this regard, transparent micromodels are suitable tools to investigate fluid flow in porous media. However, using glass as the primary material makes them inappropriate for predicting the natural behavior of rocks. Moreover, constructing these micromodels is time-consuming via conventional methods. Thus, an alternative approach can be to employ 3D printing technology to fabricate representative porous media. This study investigates fluid flow processes through a transparent microfluidic device based on a complex porous geometry (natural rock) using digital-light processing printing technology. Unlike previous studies, this one has focused on manufacturing repeatability. This micromodel, like a custom-built transparent cell, is capable of modeling single and multiphase transport phenomena. First, the tomographic data of a carbonate rock sample is segmented and 3D printed by a digital-light processing printer. Two miscible and immiscible tracer injection experiments are performed on the printed microfluidic media, while the experiments are verified with the same boundary conditions using a CFD simulator. The comparison of the results is based on Structural Similarity Index Measure (SSIM), where in both miscible and immiscible experiments, more than 80% SSIM is achieved. This confirms the reliability of printing methodology for manufacturing reusable microfluidic models as a promising and reliable tool for visual investigation of fluid flow in porous media. Ultimately, this study presents a novel comprehensive framework for manufacturing 2.5D realistic microfluidic devices (micromodels) from pore-scale rock images that are validated through CFD simulations.

Waterflooding is widely implemented to enhance oil recovery. The displacement efficiency of this method depends on multiple factors, including makeup water properties, native oil, and formation rock. The incompatibility of the injected fluid with the pore fluid can result in a chemical non-equilibrium state within porous media that may result in different chemical reactions that depend on the type and number of the existing components within the system. The present research aims to develop a mathematical model capable of handling multiple geochemical reactions to predict pH and ions concentration during an acid injection while considering alterations of rock and fluid properties. The dependency of fluid properties (viscosity and density) is considered based on the system ionic concentration, which can be more crucial in high saline media. The validity of the developed model was evaluated using the experimental literature data. The results reveal that in addition to the effect of injected fluid pH, the process efficiency can be influenced by the composition of the injected fluid. In other words, fluid properties dependency on salinity and the injected fluid composition is significant during geochemical simulations. Comparing homogenous and heterogenous mineral distribution shows an insignificant sensitivity to the amount heterogeneity while the total mineral contents remain constant in both cases.

Waterflooding in fractured reservoirs is a challenging task due to the presence of high conductive flow pathways such as fractures. Much of the injected water passes through fractures without sweeping the oil in the low permeable area, which results in an early breakthrough. Implementing deep conformance control techniques can be a remedy for this early water breakthrough. pH-sensitive microgel injection is a conformance control method in which the dependency of microgel viscosity to pH guarantees easy injection of these microgels into formations at low pH environments. Because of the geochemical reactions among rock minerals, microgels, and a pre-flushing acid, the microgel pH increases; therefore, these microgels swell and block high conductive fractures. In this study, a designed visual cell containing rock samples is implemented to observe rock–microgel interactions during a pH-sensitive microgel flooding into a fractured carbonate medium. First, the dependency of fracture aperture changes to the acid pre-flush flow rate is examined. Then, we investigate the effect of pH-sensitive microgel concentration on its resistance to block fractures during post-water flooding by studying the gel failure mechanisms (e.g., adhesive separation, cohesive failure). Finally, the effect of an initial aperture of fracture is examined on microgel washout when water injection is resumed. The results showed that both decreasing the acid flow rate and lowering the initial aperture could increase the rate of aperture changes. Moreover, the microgel solution with a concentration of 1 wt% showed the highest resistance (98.2 psi/ft) against post-water injection. Additionally, this microgel concentration had the highest permeability reduction factor. Meanwhile, the smaller initial aperture of fracture contributed to a higher microgel resistance.

This study aims to numerically determine the roles of the geochemical reactions during the injection of a strong acid into a sandstone sample. As a case study, we used laboratory results of hydrochloric acid (HCl) injection into a sandstone core plug sample from the literature. As the exact cement composition of the implemented sandstone was not available, two probable cement compositions were considered (i.e., calcite and dolomite cement). A fully-implicit model, coded in Python, was used to simulate the underlying geochemical reactions during the HCl injection (i.e., equilibrium and kinetical reactions). In addition, the reactive surface area and porosity-permeability changes of the rock sample were included in the model. The modeling results show that dolomite cement matched better than calcite cement with the experimental acidizing data. A perfect effluent pH prediction was therefore achieved when the reactive surface area was considered as a function of mineral volume fraction. Moreover, a detailed analysis of the dissolution/precipitation rate of different minerals involved in simulations was provided. The presented model improves our understanding of sandstone acidizing by determining dominant reactions. [ABSTRACT FROM AUTHOR]

The purpose of this study is to calculate Total Organic Carbon (TOC) values of the Iranian field using a combination of sonic and resistivity logs (Passay method) and neural networks method in the conditions where the core analysis or well-log measurement does not exist. We compared the resultant TOC with the ones obtained from the geochemical analysis. To correlate between the total organic carbon data and petrophysical log, which are available after logging, Multilayer Perceptron Artificial Neural Network is used. After analyzing 100 cutting samples by using rock -Eval pyrolysis, geochemical parameters have achieved. By using the multi-layer perceptron with Levenberg-Marquardt training algorithm, the TOC with correlation coefficient 0.88 and MSE 1.443 have been provided in the intervals without analyzed samples. Finally, the TOC was estimated by using separation of resistivity and the sonic log, although, with the favorable results in some other fields, the estimation had a correlation Coefficient of 51% in this field. Comparing the performance of the multi-layer perceptron with Levenberg-Marquardt training algorithm (with an accuracy of 88%) and results of the Passay method (with an accuracy of 51%) indicated that the neural network is more accurate and has better consistency compared with the empirical formula. [ABSTRACT FROM AUTHOR]