In world practice, the role of reproduction of raw material base of oil production by implementing modern methods of oil recovery enhancement (thermal, gas, chemical, microbiological) on the basis of innovative techniques and technologies is rapidly growing and is becoming more important. It is concluded that at present, the priority of increasing oil reserves in world oil production is the development and industrial introduction of modern integrated methods of enhanced oil recovery, which can provide a synergistic effect in the development of new and developed oil fields. This article presents a review and comparative analysis of theoretical and practical methods of improving oil recovery of conventional and unconventional reservoirs. The paper examines in detail methods of improving oil recovery, taking into account the factors of enhanced oil recovery of oil reservoirs. Considered the main methods and technologies currently used to develop oil fields and recommendations for their effective use, taking into account the variety of external factors of oil production: the geological structure of the reservoir, its volume, and properties of oils. It is shown that there is no universal method of oil reservoir development, and it must be chosen after a thorough feasibility study among several proposed models. When describing the methods of enhanced oil recovery, special attention is also paid to the physical processes that occur as a result of applying the technology. In conclusion, the positive and negative characteristics of the presented methods included in EOR are presented, and recommendations that may influence the choice of practical solutions for engineers and oil producers are given. Conclusions are made that development systems, placement and choice of operating mode of wells essentially depend on the geological structure of the reservoir, its volume and properties of oils. An important role in this is the construction of a geological model of the production facility. The used hydrodynamic models of development are based on physical laws, about which oil producers sometimes don’t even suspect, and the authors of the models are not always able to convey it to the real producers. The authors consider it reasonable to make a logical generalizing conclusion that understanding processes occurring in the reservoir and taking appropriate measures for optimization and intensification of oil production will allow making oil production as effective as possible.

Mining operations have a significant negative impact on the surrounding ecosystems. The operation of mines and quarries creates a large amount of waste that accumulate and are practically unrecyclable in the environment. The involvement of these wastes in economic activity is an extremely urgent task. This can make the economy more sustainable and reduce its influence on ecosystems. This work presents the attempts of using quarry wastes as a fertilizer applied for growing tea crops. The novelty of this research involves revealing the quarry wastes as a fertilizer when growing Camellia sinensis (L.) O. Kuntze and assessing changes in the productivity of this plant when applying these calcium wastes. The waste of a quarry intended for extracting crushed stone was studied in this article. The composition of the waste was analyzed. Fertilizers used for manuring the soil were prepared based on the waste. Two experimental sites were selected. One of them was a control, where Camellia sinensis (L.) O. Kuntze was grown without using fertilizers. Fertilizers obtained from the waste were applied on the second site. The experimental work proceeded for 10 years. When discussing the results, special attention was paid to climatic conditions. This was caused by the need to show that it was the use of the fertilizer that influenced the change in the yield, not the climatic conditions. As a result of using calcium fertilizers based on the waste, the productivity of Camellia sinensis (L.) O. Kuntze was increased. The application of the fertilizers based on the quarry wastes was shown to provide an increase in the yield. The possibility of using calcium fertilizers to overcome unfavorable agroclimatic conditions during the tea cultivation was also demonstrated. To assess the climatic impact of applying new fertilizers, three-dimensional modeling in the “gnuplot v.5.4” software was used. As a result, an increase in the average annual precipitation, from 1000 to 1980 mm/year, in the range of the average annual air temperature, from 14 to 16 °C, was found to lead to an increase (when using a new fertilizer) in the yield of Camellia sinensis (L.) O. Kuntze up to 4.8 times (from 20 to 95 centner/ha). The results have shown that applying fertilizers based on the quarry wastes is also possible in unfavorable climatic conditions.

Artificial neural networks are successfully used to solve a wide variety of scientific and technical problems. The purpose of the study is to increase the efficiency of distributed solutions for problems involving structural-parametric synthesis of neural network models of complex systems based on GRID (geographically disperse computing resources) technology through the integrated application of the apparatus of evolutionary optimization and queuing theory. During the course of the research, the following was obtained: (i) New mathematical models for assessing the performance and reliability of GRID systems; (ii) A new multi-criteria optimization model for designing GRID systems to solve high-resource computing problems; and (iii) A new decision support system for the design of GRID systems using a multi-criteria genetic algorithm. Fonseca and Fleming’s genetic algorithm with a dynamic penalty function was used as a method for solving the stated multi-constrained optimization problem. The developed program system was used to solve the problem of choosing an effective structure of a centralized GRID system that was configured to solve the problem of structural-parametric synthesis of neural network models. To test the proposed approach, a Pareto-optimal configuration of the GRID system was built with the following characteristics: average performance–103.483 GFLOPS, cost–500 rubles per day, availability rate–99.92%, and minimum performance–51 GFLOPS.

This article is a compilation of interdisciplinary studies aimed at ensuring the environmental, political, and economic sustainability of oil and gas-producing countries with a focus on areas with many years of permafrost. One of the main concepts adopted in this research was the desire to show that confronting various energy lobbies is not mandatory and that it is necessary to find compromises by finding and introducing innovative technologies for integrated development for the benefit of society, industry, and the state. This is particularly relevant due to the increasing share of hard-to-recover hydrocarbon reserves, widely represented in the fields of the Eastern Siberian Arctic, and because Russia is the leader in flare emissions. We thus present the relevance of using these gases as industrial waste while reducing the carbon footprint. The technology of biofuel production based on the use of supercritical liquid extraction in a well extractor is presented as a result of the development of the presented experimental devices representing the investigation of the processes of extraction in wells and reactors for the distillation of hydrocarbons from heavy oil components. The obtained yield of the desired product (hydrogen) of the thermocatalytic pyrolysis of the test extract was in the range of 44 to 118 L/h, depending on the catalyst. This information can help inform the direction of future ecological engineering activities in the Eastern Siberian Arctic region.

The present study is focused on improving the technology for deep oil sludge processing by pyrolysis methods, considered to be the most promising technology for their environmentally friendly utilization, in which a significant yield of fuel products is expected. The technology developed by the authors of this study is a two-stage process. The first stage, pyrolysis of oil sludge, was investigated in previous papers. A significant yield of non-condensable gases was obtained. This paper presents a study of the second stage of complex deep processing technology—pyrolysis of non-condensable gases (purified propane) using a stirrer with the help of the developed experimental setup. The expected benefit of using the stirrer is improved heat transfer due to circumferential and radial-axial circulation of the gas flow. The effect of a stirrer on the yield of final target decomposition products—H₂-containing mixtures and H₂ generated during non-catalytic (medium-temperature) and catalytic pyrolysis of non-condensable gases obtained by pyrolysis of oil sludge are estimated. Ni catalyst was used for catalytic pyrolysis. The study shows that the application of the stirrer leads to increasing in H₂-containing mixtures and H₂ concentrations. In particular, during the whole reaction time (10 h), the average H₂ concentration in pyrolysis gas during catalytic pyrolysis increased by ~5.3%. In this case, the optimum reaction time to produce H₂ was 4 h. The peak H₂ concentration in the pyrolysis gas at reaction temperature 590 ± 10 °C was: 66.5 vol. % with the stirrer versus 62 vol. % without the stirrer with an error of ±0.4 %. A further increase in reaction time is cost-effective in order to obtain H₂-containing mixtures.

This paper discusses the problem of choosing the effective process parameters of electron beam welding (EBW). To that end, the research team has developed a mathematical model that applies machine learning to predict the effective process parameters. Since predicting process parameters requires a regression model, this research uses regression analysis algorithms such as the ridge regression and the random forest regressor. The paper analyzes whether these algorithms are applicable to the problem and tests the accuracy of their predictions. To generalize the approach and strengthen the justification of choosing the hyperparameters of the regression algorithms studied herein and considering the high variability of these hyperparameters, the multiobjective optimization technique applicable for this combinatorial problem - an (evolutionary) genetic algorithm - is proposed to determine effective sets of hyperparameters. All the models successfully addressed the task, achieving a forecasting accuracy of at least 89%. The article presents the final form of the ridge regression model describing the dependence of the weld’s depth and width: for the weld depth, there is a 2nd degree polynomial dependence with a regularization of 10 ⁻⁵ , and for the weld width, there is a 3rd degree polynomial dependence with a regularization of 10 ⁻⁴ . An automated system based on this approach that accurately predicts the process parameters is proposed herein. In addition to performing basic modeling functions, the proposed system allows the visualization of the model-predicted data in the form of an interactive plot. This function could be useful for technologists by allowing them to determine the process parameters that ensure the required weld dimensions. Adopting the proposed EBW parameter prediction method in practice will provide decision support for cases when engineers need to test the EBW process or to start making new products.