The article shows studies of the problem of active sediment formation during mixing of residual fuels, caused by the manifestation of incompatibility. To preserve the quality and reduce sediment formation during transshipment, storage, and transportation of marine residual fuels, a laboratory method for determining the compatibility and stability of fuels has been developed, which makes it possible to determine the quantitative characteristics of the sediment formation activity. According to the method developed, laboratory studies have been carried out to determine incompatible fuel components and the influence of composition on the sedimentation process. Tests were carried out to determine the quality indicators and the individual group composition of the fuel samples. Based on the results of the studies, the dependences of the influence of normal structure paraffins in the range from 55 to 70 wt. % and asphaltenes in the range from 0.5 to 3.5 wt. % in the fuel composition on the sedimentation activity due to incompatibility were obtained. To obtain a convenient tool that is applicable in practice, a nomogram has been developed on the basis of the dependences obtained experimentally. It was also determined that, after reaching the maximum values of sediment formation with a further increase in the content of n-paraffins, saturation is observed, and the value of the sediment content remains at the same level. Maximum total sediment values have been found to depend on asphaltene content and do not significantly exceed them within 10%. The results of the research presented in this article allow laboratory and calculation to determine the possibility of incompatibility and to preserve the quality of marine residual fuels.

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.

Providing quality fuel to ships with reduced SOx content is a priority task. Marine residual fuels are one of the main sources of atmospheric pollution during the operation of ships and sea tankers. Hence, the International Maritime Organization (IMO) has established strict regulations for the sulfur content of marine fuels. One of the possible technological solutions allowing for adherence to the sulfur content limits is use of mixed fuels. However, it carries with it risks of ingredient incompatibilities. This article explores a new approach to the study of active sedimentation of residual and mixed fuels. An assessment of the sedimentation process during mixing, storage, and transportation of marine fuels is made based on estimation three-dimensional diagrams developed by the authors. In an effort to find the optimal solution, studies have been carried out to determine the influence of marine residual fuel compositions on sediment formation via machine learning algorithms. Thus, a model which can be used to predict incompatibilities in fuel compositions as well as sedimentation processes is proposed. The model can be used to determine the sediment content of mixed marine residual fuels with the desired sulfur concentration.

According to the top-priority trends and challenges in the mineral sector, and as per the mining science strategy, it is highly critical to arrange enhanced control, prediction and safety of production objects and their functioning for the preservation of automation sustainability. Improved control of databases, regulatory bonds, management, logistics and principles of sustainable development in mining makes it possible to reduce technological deviations and accidents at large mining and processing plants. Most procedural violations and accidents in surface and underground mines occur because of the unskilled actions of process flow operators. Damage in this case can be considerable, especially as compared with the expenses connected with qualitative training and persistent development of personnel engaged with supervisory control and data acquisition for the efficient operation of SCADA-systems within the automation framework of mining and processing plants. Definition of digital systems and their interrelation with multilevel automated control can be incorrect. The review of new principles can awaken interest in the conceptual assessment of digitalization processes using such notions as: numerical models, simulator, and artificial intelligence. Often applied formulations and principles of a digital model are substituted without justification of functional connections. On the other hand, a digital system today can be assumed as robotic lines and other numerical models and smart technologies, for instance, machining stations with numerical program control. It is necessary to define the practical significance of conceptual modifications and digital transformation regarding objects of the mineral sector, using Big Data; to understand how a digital twin can influence a changeable process situation; to provide prompt prediction; to eliminate an accident; and to preserve the physical balance in the whole production system. Such intelligent and flexible productions particularly need computerbased simulators and digital twins based on technologies of Industry 4.0–extended and virtual reality on the basis of digital twins. Digital twins allow maximal simulation of real-life activity of process flow operators. The skills acquired by personnel after such simulation training enable operators to master the optimized procedure for functioning in emergency situations in mineral mining and processing. This paper exemplifies the remote training and control of process flows, which is of concern in the current international situation.

With the depletion of traditional energy resources, the share of heavy-oil production has been increasing recently. According to some estimates, their reserves account for 80% of the world’s oil resources. Costs for extraction of heavy oil and natural bitumen are 3–4 times higher than the costs of extracting light oil, which is due not only to higher density and viscosity indicators but also to insufficient development of equipment and technologies for the extraction, transportation, and processing of such oils. Currently, a single pipeline system is used to pump both light and heavy oil. Therefore, it is necessary to take into account the features of the heavy-oil pumping mode. This paper presents mathematical models of heavy-oil flow in oil-field pipelines. The rheological properties of several heavy-oil samples were determined by experiments. The dependencies obtained were used as input data for a simulation model using computational fluid dynamics (CFD) methods. The modeling condition investigates the range of shear rates up to 300 s−1. At the same time, results up to 30 s−1 are considered in the developed computational models. The methodology of the research is, thus, based on a CFD approach with experimental confirmation of the results obtained. The proposed rheological flow model for heavy oil reflects the dynamics of the internal structural transformation during petroleum transportation. The validity of the model is confirmed by a comparison between the theoretical and the obtained experimental results. The results of the conducted research can be considered during the selection of heavy-oil treatment techniques for its efficient transportation.