Modeling of Heavy-Oil Flow with Regard to Their Rheological Properties

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.publishedVersio

Exploring of the incompatibility of marine residual fuel: A case study using machine learning methods

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.publishedVersio

Research of the influence of marine residual fuel composition on sedimentation due to incompatibility

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.publishedVersio

Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

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.publishedVersio

Modeling of Heavy-Oil Flow with Regard to Their Rheological Properties

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

Features of the Process Obtaining of Mg-Zn-Y Master Alloy by the Metallothermic Recovery Method of Yttrium Fluoride Melt

At present, magnesium master alloys with such rare earth metals (REM) as yttrium are used in the production of alloys of magnesium and aluminum. These alloys especially the system Mg-6Zn-1Y-0,5Zr are commonly used in the aircraft and automotive industries. The article is devoted to the exploration of the synthesis process features for ternary magnesium master alloys with yttrium and zinc. The authors used X-ray fluorescence analysis (XRF), differential thermal analysis (DTA), and X-ray spectral analysis (XRD). Optical microscopy was used to conduct microstructural studies. The thermal effects that occur during metallothermic reactions of yttrium reduction from the YF3-NaCl-KCl-CaCl2 salt mixture with a melt of magnesium and zinc were investigated, and the temperatures of these effects were determined. It has been confirmed that the metallothermic reaction of yttrium reduction proceeds from the precursors of the composition: Na1.5Y2.5F9, NaYF4, Na5Y9F32, and KY7F22, and starts at a temperature of 471 °C. The results of experimental studies of the process of metallothermic reduction of yttrium from the salt mixture YF3-NaCl-KCl-CaCl2 are presented in detail. These experiments were carried out in a pit furnace at temperatures ranging from 650 to 700 °C, and it was found that, at a synthesis temperature of 700 °C, the yttrium yield is up to 99.1–99.8%. The paper establishes rational technological regimes for the synthesis (temperature 700 °C, exposure for 25 min, the ratio of chlorides to yttrium fluoride 6:1, periodic stirring of the molten metal) at which the yttrium yield reaches up to 99.8%. The structure of the master alloy samples obtained during the experiments was studied. That structure can be distinguished by a uniform distribution of ternary intermetallic compounds (Mg3YZn6) in the bulk of the double magnesium–zinc eutectic. Studies have been carried out on testing the obtained ternary master alloy as an alloying material in the production of alloys of the Mg-6Zn-1Y-0.5Zr system, while the digestibility of yttrium ranged from 91 to 95%

Features of the Process Obtaining of Mg-Zn-Y Master Alloy by the Metallothermic Recovery Method of Yttrium Fluoride Melt

At present, magnesium master alloys with such rare earth metals (REM) as yttrium are used in the production of alloys of magnesium and aluminum. These alloys especially the system Mg-6Zn-1Y-0,5Zr are commonly used in the aircraft and automotive industries. The article is devoted to the exploration of the synthesis process features for ternary magnesium master alloys with yttrium and zinc. The authors used X-ray fluorescence analysis (XRF), differential thermal analysis (DTA), and X-ray spectral analysis (XRD). Optical microscopy was used to conduct microstructural studies. The thermal effects that occur during metallothermic reactions of yttrium reduction from the YF3-NaCl-KCl-CaCl2 salt mixture with a melt of magnesium and zinc were investigated, and the temperatures of these effects were determined. It has been confirmed that the metallothermic reaction of yttrium reduction proceeds from the precursors of the composition: Na1.5Y2.5F9, NaYF4, Na5Y9F32, and KY7F22, and starts at a temperature of 471 °C. The results of experimental studies of the process of metallothermic reduction of yttrium from the salt mixture YF3-NaCl-KCl-CaCl2 are presented in detail. These experiments were carried out in a pit furnace at temperatures ranging from 650 to 700 °C, and it was found that, at a synthesis temperature of 700 °C, the yttrium yield is up to 99.1–99.8%. The paper establishes rational technological regimes for the synthesis (temperature 700 °C, exposure for 25 min, the ratio of chlorides to yttrium fluoride 6:1, periodic stirring of the molten metal) at which the yttrium yield reaches up to 99.8%. The structure of the master alloy samples obtained during the experiments was studied. That structure can be distinguished by a uniform distribution of ternary intermetallic compounds (Mg3YZn6) in the bulk of the double magnesium–zinc eutectic. Studies have been carried out on testing the obtained ternary master alloy as an alloying material in the production of alloys of the Mg-6Zn-1Y-0.5Zr system, while the digestibility of yttrium ranged from 91 to 95%

Development MPC for the Grinding Process in SAG Mills Using DEM Investigations on Liner Wear

The rapidly developing mining industry poses the urgent problem of increasing the energy efficiency of the operation of basic equipment, such as semi-autogenous grinding (SAG) mills. For this purpose, a large number of studies have been carried out on the establishment of optimal operating parameters of the mill, the development of the design of lifters, the rational selection of their materials, etc. However, the dependence of operating parameters on the properties of the ore, the design of the linings and the wear of lifters has not been sufficiently studied. This work analyzes the process of grinding rock in SAG mill and the wear of lifters. The discrete element method (DEM) was used to simulate the grinding of apatite-nepheline ore in a mill using different types of linings and determining the process parameters. It was found that the liners operating in cascade mode were subjected to impact-abrasive wear, while the liners with the cascade mode of operation were subjected predominantly to abrasive wear. At the same time, the results showed an average 40–50% reduction in linear wear. On the basis of modelling results, the service life of lifters was calculated. It is concluded that the Archard model makes it possible to reproduce with sufficient accuracy the wear processes occurring in the mills, taking into account the physical and mechanical properties of the specified materials. The control system design for the grinding process for SAG mills with the use of modern variable frequency drives (VFD) was developed. With the use of the proposed approach, the model predictive control (MPC) was developed to provide recommendations for controlling the optimum speed of the mill drum rotation

Exploring of the incompatibility of marine residual fuel: A case study using machine learning methods

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

Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

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