In order to establish the formation patterns of the Co–Mg oxide system, samples with different Co:Mg ratios and heat treatment temperatures were synthesized and studied. A study of the samples confirmed the phase transition of Mg_xCo_2–xO₄ spinels into the corresponding solid solutions at 800–900 °C. The similarity of the formation patterns for different compositions is shown. The rocksalt oxide in low-temperature samples is an anion-modified paracrystalline phase that forms a “true” solid solution only upon spinel decomposition. The TPR profiles of the decomposed Co₃O₄ spinel show surface Co₃O₄ peaks and a wide peak corresponding to the well-crystallized CoO, while partial Co₃O₄ TPR up to 380 °C results in dispersed and amorphous CoO. The high-temperature non-stoichiometric samples are poorly reduced, indicating their low oxygen reactivity. Spinel reoxidation after heat treatment to 1100 °C by calcination at 750 °C showed complete regeneration for MgCo₂O₄–Co₃O₄ samples and its absence in case of an excess of MgO relative to stoichiometry.

In order to establish the formation patterns of the Co–Mg oxide system, samples with different Co:Mg ratios and heat treatment temperatures were synthesized and studied. Study of the sam-ples confirmed the phase transition of MgxCo2–xO4 spinels into corresponding solid solutions at 800–900 °C. The similarity of formation patterns for different compositions is shown. The rock-salt oxide in low-temperature samples is an anion-modified paracrystalline phase that forms a “true” solid solution only upon spinel decomposition. TPR profiles of decomposed Co3O4 spinel show surface Co3O4 peaks and a wide peak corresponding to well-crystallized CoO, while par-tial Co3O4 TPR up to 380 °C results in dispersed and amorphous CoO. High-temperature non-stoichiometric samples are poorly reduced, indicating their low oxygen reactivity. Spinel reoxidation after heat treatment to 1100 °C by calcination at 750 °C showed complete regenera-tion for MgCo2O4–Co3O4 samples and its absence in case of an excess of MgO relative to stoi-chiometry. Based on the data obtained, it can be concluded that the processes governing the formation of Co–Mg oxide systems are determined by both Co:Mg ratio and the heat treatment conditions, potentially providing the possibility of fine tuning for specific catalytic processes.

The practice of capturing, storing and utilizing CO2 is becoming key to developing sustainable energy and industrial solutions. The technology promotes the use of fossil fuels, which remain the predominant source of energy worldwide. The effectiveness of the technology is evident in the reduction of CO2 levels in the atmosphere, a significant contribution to the reduction of global greenhouse gas (GHG) emissions and the fight against climate change. However, a notable concern in the realm of geological storage revolves around the potential leakage of CO2 from storage reservoirs. Carbon dioxide has the capability to migrate from the storage site, reaching both the surface and underground formations. Surface leakage presents health risks to humans, animals, and plants. The solution to this problem requires a detailed approach and should be solved through an inverse problem, in which pressure measurements in monitoring wells will be performed frequently to obtain information about the reservoir and possible leaks. Additionally, there are a number of issues with carbon dioxide leakage during oil and gas extraction, as well as various operations at fields. Emphasizing the monitoring of CO2 leakage, this paper underscores the importance of developing an algorithm designed to proactively prevent CO2 leakage in aquifers and depleted reservoirs. Such an initiative is pivotal in the broader context of mitigating greenhouse gas emissions. The paper offers an overview of methodologies for effective monitoring, management and modeling of CO2 leakage and practical approaches to calculation and assessment, contributing to a more complete understanding of the challenges associated with CO2 storage.

The article presents the results of an analysis of geological, geophysical, and hydrogeological data accumulated to date for the Ili sedimentary basin to identify potential geological structures capable of trapping and storing carbon dioxide (СО2 ) emitted by industrial facilities in Almaty and the Almaty Region under geographically favorable and economically viable conditions. Data from seismic exploration, drilling, and well studies were used to study the features of the geological structure of deep stratigraphic horizons and the structural-tectonic conditions of their occurrence. Based on the results of geological modeling in the Petrel software, structural, lithological, and petrophysical models were constructed, which made it possible to determine the effective reservoir thicknesses of the Jurassic storage formation capable of trapping СО2 . Based on well data, a quantitative assessment of the distribution of reservoir properties (porosity and permeability) for the Jurassic deposits was performed. Their effective pore volume was calculated, reaching 2,918.6 thousand m3. The hydrodynamic conditions at the depth intervals of the Miocene-Paleogene and Jurassic sandstones exceed the critical parameters for carbon dioxide, at which СО2 transitions into a supercritical fluid state. This allows these horizons to be considered as reservoirs capable of absorbing and ensuring the long-term, safe storage of СО2.

Lumping methods for oil components require a more detailed approach to refine the description of fluids in an oil production area. These methods allow you to gradually increase the dynamics of the behavior of hydrocarbon deposits and increase their reserves. To date, the lumping method is relevant as an attractive way to spend on reservoir compositional modeling. The authors of the article describe PVT-calculations that have been developed for many years of illness, in the treatment of diseases of the cardiovascular system. They explain the approaches of different lumping methods. This article discusses different types of lumping methods such as classical, statistical, and machine learning based lumping.