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Well construction is the most expensive stage of oil field development. Oil and gas companies carefully calculate all possible expenditures to assure that the project of well construction and exploitation will be profitable in time. The time for every operation of well construction is strictly regulated. However, in different oil and gas companies time allotted, for example, cementing surface casing or tripping operations can differ significantly. Therefore, some oil and gas companies are unable to calculate their well construction expenses accurately and lose a significant amount of money, which can be reduced. Also, engineering teams spend a substantial amount of time developing a drilling plan for the drilling crew, which may also be fully or partially developed by automated means. We present a data-driven approach for automatic planning and scheduling of drilling operations on wells with similar design and geological characteristics, suggesting also an improved operation classification based on the IADC dictionary of drilling operations.The model for drilling operations planning is based on the directed graph traversal and process mining techniques. The algorithm takes as an input an undetailed plan, the section which is planned to be drilled, and the phase of well construction (drilling, cementing, logging). The algorithm selects the shortest path between two adjacent operations, gathers all paths together, and outputs a detailed plan with the corresponding time for each operation. For directed graph construction, we processed about 15 drilling reports from wells of a particular oil field which have similar well design and geology conditions by virtue of their geological proximity.Algorithm performance was estimated by comparing graph time against similar plan time which was calculated based on the median time of every operation in a whole dataset. Median time (P50 percentile) was used because it demonstrates objective time in terms of well construction operations. We conjecture the above based on the fact that in some cases time for tripping can be faster or slower in some wells due to geological or other conditions, and the median provides an outlier-robust estimate of the average value. Also, graph time was compared between the engineering team's proposed plan and the actual time from drilling reports. Graph construction quality was estimated using three principal metrics: the Jaccard coefficient, structural distance, and fitness similarity.

Summary Monitoring of production rates is essential for reservoir management, history matching, and production optimization. Traditionally, such information is provided by multiphase flowmeters or test separators. The growth of the availability of data, combined with the rapid development of computational resources, enabled the inception of digital techniques, which estimate oil, gas, and water rates indirectly. This paper discusses the application of continuous deep learning models, capable of reproducing multiphase flow dynamics for production monitoring purposes. This technique combines the time evolution properties of a dynamical system and the ability of neural networks to quantitively describe poorly understood multiphase phenomena and can be considered as a hybrid solution between data-driven and mechanistic approaches. The continuous latent ordinary differential equation (Latent ODE) approach is compared with other known machine learning methods, such as linear regression, ensemble-based model, and recurrent neural network (RNN). In addition, the framework of virtual flowmetering (VFM) is analyzed from the point of multiphase measurement technology. In this work, the application of Latent ODEs for the problem of multiphase flow rate estimation is introduced. The considered example refers to a scenario in which the topside oil, gas, and water flow rates are estimated using the data from several downhole pressure sensors. The predictive capabilities of different types of machine learning and deep learning instruments are explored using simulated production data from a multiphase flow simulator. The results demonstrate the satisfactory performance of the continuous deep learning models in comparison with other machine learning methods in terms of accuracy, where the normalized root-mean-squared error (RMSE) and mean absolute error (MAE) of prediction below 5% were achieved. While Latent ODE demonstrates the significant time required to train the model, it outperforms other methods for irregularly sampled time series, which makes it especially attractive to forecast values of multiphase rates.

Over a long period of operation, under the influence of corrosion and stresses from the acting forces, metal structures lose their strength. There is a need for their periodic non-destructive testing. The development of new methods is relevant in the field of control of building metal structures, such as bridge structures, structures of building cranes and other mechanical engineering products. The applied methods should be reliable and should not require huge material and labor costs. In this work, informational relationships between acoustic characteristics and parameters of metal microstructure are established. The proposed method can be useful for specialists and experts in the field of monitoring the technical condition of metal products requiring non-destructive testing. The safety of the operated objects depends on the accuracy of the applied criteria, as well as the degree of resource saving due to the full use of the product resource.

Yanko Roman, Levashov Mikhail, Chaka Elena, Safonov Sergey. Morphofunctional state of the lungs respiratory part in normotensive and hypertensive rats after combined exposure to intermittent hypoxia and melatonin. Journal of Education, Health and Sport. 2021;11(1):56-68. eISSN 2391-8306. DOI http://dx.doi.org/10.12775/JEHS.2021.11.1.006 https://apcz.umk.pl/czasopisma/index.php/JEHS/article/view/JEHS.2021.11.1.006 https://zenodo.org/record/4445108 The journal has had 5 points in Ministry of Science and Higher Education parametric evaluation. § 8. 2) and § 12. 1. 2) 22.02.2019. © The Authors 2021; This article is published with open access at Licensee Open Journal Systems of Nicolaus Copernicus University in Torun, Poland Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author (s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non commercial license Share alike. (http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. The authors declare that there is no conflict of interests regarding the publication of this paper. Received: 28.12.2020. Revised: 05.01.2021. Accepted: 15.01.2021. Morphofunctional state of the lungs respiratory part in normotensive and hypertensive rats after combined exposure to intermittent hypoxia and melatonin Roman Yanko (B,C,E,F), Mikhail Levashov (A, D), Elena Chaka (B,F), Sergey Safonov (G) A. A. Bogomoletz Institute of Physiology, National Academy of Sciences of Ukraine, Department of Clinical Physiology of Connective Tissue, Kiev, Ukraine, Authors’ contribution / Wkład autorow: A. Study design, planning / zaplanowanie badań B. Data collection entry / zebranie danych C. Data analysis, statistics / dane analiza i statystyki D. Data interpretation / interpretacja danych E. Preparation of manuscript / przygotowanie artykułu F. Literature analysis, search / wyszukiwanie i analiza literatury G. Funds collection / zebranie funduszy Corresponding Author: Yanko Roman, A.A. Bogomoletz Institute of Physiology, Bogomoletz str., 4, Kiev, Ukraine, 01024 (e-mail: biolag@ukr.net) ORCIDs: Roman Yanko https://orcid.org/0000-0002-0397-7517 Mikhail Levashov https://orcid.org/0000-0003-1354-2047 Elena Chaka https://orcid.org/0000-0001-7425-2751 Sergey Safonov https://orcid.org/0000-0002-4785-0315 Abstract Aim. The purpose of this work was to study and compare the combined effect of intermittent normobaric hypoxia (INH) and melatonin on the morphological and biochemical indices of the lungs respiratory part in Wistar and spontaneous hypertensive rats (SHR). Material and methods. The studies were conducted on 48 young male rats Wistar and SHR lines. The experimental rats were daily exposed to hypoxic gas mixture (12 % oxygen in nitrogen) in intermittent mode: 15 min deoxygenation / 15 min reoxygenation for 2 hours. Animals also received oral melatonin at a dose of 5 mg / 1 kg of body weight once daily. Results. The sizes of alveolus were reduced, but their number and placement density were increased in SHR rats after a combined effect of INH and melatonin. The results showed a significant decrease of the interalveolar septum thickness and a tendency to decrease of the hydroxyproline concentration in the lung tissue of the experimental rats, regardless of their line. Conclusions. The 28-day combined effect of INH and melatonin increase the total alveolar surface area and reduce the amount of connective tissue in the lungs of experimental rats. These changes can improve the efficiency of the intrapulmonary gas exchange processes. Key words: intermittent normobaric hypoxia; melatonin; lungs.

Yanko Roman, Levashov Mikhail, Chaka Elena, Litovka Irina, Safonov Sergey. Seasonal features of the combined effects of intermittent normobabic hypoxia and melatonin on the thyroid gland morphofunctional state. Journal of Education, Health and Sport. 2020;10(4):186-198. eISSN 2391-8306. DOI http://dx.doi.org/10.12775/JEHS.2020.10.04.021 https://apcz.umk.pl/czasopisma/index.php/JEHS/article/view/JEHS.2020.10.04.021 https://zenodo.org/record/3770209 The journal has had 5 points in Ministry of Science and Higher Education parametric evaluation. § 8. 2) and § 12. 1. 2) 22.02.2019. © The Authors 2020; This article is published with open access at Licensee Open Journal Systems of Nicolaus Copernicus University in Torun, Poland Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author (s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non commercial license Share alike. (http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited. The authors declare that there is no conflict of interests regarding the publication of this paper. Received: 17.02.2020. Revised: 20.04.2020. Accepted: 27.04.2020. Seasonal features of the combined effects of intermittent normobabic hypoxia and melatonin on the thyroid gland morphofunctional state Roman Yanko (B,C,E,F), Mikhail Levashov (A, D), Elena Chaka (B,F), Irina Litovka (A), Sergey Safonov (G) A. A. Bogomoletz Institute of Physiology, National Academy of Sciences of Ukraine, Department of Clinical Physiology of Connective Tissue, Kiev, Ukraine Authors’ contribution / Wkład autorow: A. Study design, planning / zaplanowanie badań B. Data collection entry / zebranie danych C. Data analysis, statistics / dane analiza i statystyki D. Data interpretation / interpretacja danych E. Preparation of manuscript / przygotowanie artykułu F. Literature analysis, search / wyszukiwanie i analiza literatury G. Funds collection / zebranie funduszy Corresponding Author: Yanko Roman, A.A. Bogomoletz Institute of Physiology, Bogomoletz str., 4, Kiev, Ukraine, 01024 (e-mail: biolag@ukr.net) Roman Yanko https://orcid.org/0000-0002-0397-7517 Mikhail Levashov https://orcid.org/0000-0003-1354-2047 Elena Chaka https://orcid.org/0000-0001-7425-2751 Irina Litovka http://orcid.org/ 0000-0001-9163-3572 Sergey Safonov https://orcid.org/0000-0002-4785-0315 Abstract The aim of this work is to compare the effect of the combination influence of dosed intermittent normobaric hypoxia and melatonin in different seasons of the year on the morphofunctional state of the rats’ thyroid gland. Material and methods. The study was carried out on 48 male rats of the Wistar line. The hypoxic gas mixture (12% O2 and 88% N2) was daily given to experimental animals in intermittent mode: 15 minutes deoxygenation / 15 minutes reoxygenation for 2 hours. Melatonin was administered orally in a dose of 5 mg / kg at 10.00 a.m. The total duration of the experiment was 28 days. Histological preparations were prepared according to a standard methodic. Histomorphometry of the digital images of preparations was carried out using the computer program «Image J». Results. It was shown that the thyroid gland of rats reacted differently to the combined effects of intermittent hypoxia and melatonin at different seasons. So the signs of the gland activity were increased after influence of hypoxia and melatonin in spring. It was evidenced by a smaller area of follicles and colloid, a smaller internal diameter of the follicles and colloid-accumulation index, a greater height of the thyrocytes and follicular-colloid index, a smaller width of the interlobar, interlobular and interfollicular connective tissue interlayers compared to the control. The prolonged exposure to intermittent hypoxia and melatonin in autumn decreased the area and height of the follicular epithelium, increased the internal diameter of the follicles, decreased the follicular-colloidal index and increased the colloid-accumulation index. This indicated a decrease in the activity of the thyroid gland. Conclusions. Thus, in the spring, the morphofunctional activity of the thyroid gland is moderately increased, and in the autumn period, on the contrary, decreases. Key words: intermittent normobaric hypoxia; melatonin; thyroid gland.

Modern challenges in reservoir management have recently faced new opportunities in production control and optimization strategies. These strategies in turn rely on the availability of monitoring equipment, which is used to obtain production rates in real-time with sufficient accuracy. In particular, a multiphase flow meter is a device for measuring the individual rates of oil, gas and water from a well in real-time without separating fluid phases. Currently, there are several technologies available on the market but multiphase flow meters generally incapable to handle all ranges of operating conditions with satisfactory accuracy in addition to being expensive to maintain. Virtual Flow Metering (VFM) is a mathematical technique for the indirect estimation of oil, gas and water flowrates produced from a well. This method uses more readily available data from conventional sensors, such as downhole pressure and temperature gauges, and calculates the multiphase rates by combining physical multiphase models, various measurement data and an optimization algorithm. In this work, a brief overview of the virtual metering methods is presented, which is followed by the application of several advanced machine-learning techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore. The predictive capabilities of different types of machine learning instruments are explored using a model simulated production data. Also, the effect of measurement noise on the quality of estimates is considered. The presented results demonstrate that the data-driven methods are very capable to predict multiphase flow rates with sufficient accuracy and can be considered as a back-up solution for a conventional multiphase meter.

Well log analysis, through deploying advanced artificial intelligence (AI) algorithms, is key for wellbore geological studies. By analyzing different well characteristics with modern AI tools it becomes possible to estimate interwell saturation with improved accuracy, outlining primary fluid channels and saturation propagations in the reservoirs interwell region. The development of modern deep learning and artificial intelligence methods allows analysts to predict interwell saturation as a function of observed data in the near wellbore logged geological layers. This work addresses the use of deep neural network architectures as well as tensor regression models for predicting interwell saturation from other well characteristics, such as resistivity and porosity, as well as local near-well saturation. Several algorithms are compared in terms of both accuracy and computational efficiency. Sensitivity analysis for model parameters is carried out, which is based on the wells’ geometry, radius, and multiple sampling techniques. Additionally, the impact of local saturation prior knowledge on the model accuracy is analyzed. A reservoir box model encompassing volumetric interwell porosity, resistivity and saturation data was utilized for the validating and testing of the AI algorithms. A prototype is developed with Python 3.6 programming language.

Combining physics-based models for well log analysis with artificial intelligence (AI) advanced algorithms is crucial for wellbore studies. Data-driven methods do not generalize well and lack theoretical knowledge accumulated in the field. Estimating well saturation significantly improves if predictions from physical models are used to constrain data-driven algorithms in outlined primary fluid channels and other important points of interest. Saturation propagations in the reservoirs interwell region also generalize better under using combination of models. This work addresses combined usage of theoretical and data-driven models by aggregating them into single hybrid model. Multiple physical and data-driven models are under study, their parameters are optimized using observations. Weighted sum is used to predict water saturation at every point with weights being recomputed at each step. Model outputs are compared in terms accuracy and cumulative loss. A synthesized reservoir box model encompassing volumetric interwell porosity, resistivity and saturation data is used for the validation of the algorithms. Aggregated model for estimating interwell saturation shows improved prediction accuracy compared both to physics-based or data-driven approaches separately.

Multiphase flow metering is an important tool for production monitoring and optimization. Although there are many technologies available on the market, the existing multiphase meters are only accurate to a certain extend and generally are expensive to purchase and maintain.Virtual flow metering (VFM) is a low-cost alternative to conventional production monitoring tools, which relies on mathematical modelling rather than the use of hardware instrumentation. Supported by the availability of the data from different sensors and production history, the development of different virtual flow metering systems has become a focal point for many companies.This paper discusses the importance of flow modelling for virtual flow metering. In addition, main data-driven algorithms are introduced for the analysis of several dynamic production data sets. Artificial Neural Networks (ANN) together with advanced machine learning methods such as GRU and XGBoost have been considered as possible candidates for virtual flow metering. The obtained results indicate that the machine learning algorithms estimate oil, gas and water rates with acceptable accuracy. The feasibility of the data-driven virtual metering approach for continuous production monitoring purposes has been demonstrated via a series of simulation-based cases. Amongst the used algorithms the deep learning methods provided the most accurate results combined with reasonable time for model training.

Summary Traditional reservoir and production engineering methods are based on mathematical modeling of multiphase flow in an area constrained by the petroleum oilfield region and a number of wells. These transport phenomena are governed by a series of partial differential equations (PDEs) describing the conservation of mass, momentum, and energy formulated for each considered phase. The use of modern computational methods ensures that simulating a hydrocarbon system provides an accurate prediction of flow variables, which is only valid if the underlying models are perfect. Since any model is only an approximation of reality, the results of numerical modeling tend to deviate with time and hinder proper decision-making related to field development and management. An alternative method is proposed, which identifies the general form of governing equations using transient data, which are collected over a certain time interval in a discrete number of measurement locations. The obtained equations are then refined using traditional data assimilation techniques. These two components are integrated into the universal modeling platform, where the equations governing multiphase flow and non-linear coefficient are obtained only from the data without any initial knowledge of system dynamics.

Summary Accurate and consistent measurements of multiphase rates allow operators to make decisions for better reservoir characterization, production monitoring and reservoir management. The availability of such measurements is strongly related to the performance of monitoring equipment which is used to obtain oil, gas and water flow rates from each well. Currently, there are two industry established approaches, where the flow rates are measured by a test separator or a multiphase flow meter. On the other hand, the increased availability of cost-efficient downhole sensors raised dramatically the amount of data obtained from a wellbore. A virtual flow meter (VFM) is a computational model which enables estimation of multiphase production rates from the available raw data without measuring flow rates directly. VFM systems can be considered as a cheaper alternative to the conventional multiphase flow measurements, as they do not require additional hardware deployment. The goal of the present work is to demonstrate the predictive capabilities of VFM using the Dynamic Mode Decomposition (DMD). A synthetic example is considered, describing multiphase slug flow in a horizontal well with a riser, where downhole pressure and temperature measurements are used to predict topside rates of liquid and gas.

The paper reports the consideration of the matters of product quality increase by technological methods through the way of operation characteristics support of science intensive products under development at the stage of manufacturability improvement in the course of their mastering and introduction into production. A new approach to the choice of technological methods is shown to achieve the required properties through modification of a surface layer is shown for that purpose there is developed a system of choice among technological methods used and a purposefulness for new technological methods creation. A principle of utility with a criterion estimate of the most efficient processing options to support the required operation values of a product is assumed as a basis.

In the work it is shown that the design of combined technological processes ensuring the maximum possible operating characteristics of products assessed in a criterion way is possible. The science-based methods of choice and use of technologies created earlier, methods for the development of promising coating types, means for the modification of a surface layer and thickness changes of coatings with operating characteristics required are shown. The possibilities are considered for the creation of new methods realizing operating indices the fulfillment of which is impossible at common technologies. The well-known researches have shown that at the operation of science intensive products the mechanical characteristics including fatigue material resistance depend to a great extent upon a state and properties of a surface layer that is why there is offered a substantiated choice and design of technological processes for manufacturing products with properties corresponding most completely to operation conditions.

The paper addresses the issues, related to the preparation of pipeline docking elements during the repair and recovery of pipelines in field conditions with a limited scope of technological equipment. It has been shown that the replacement of traditional processing methods by combined types of preparation of replaceable pipeline sections for welding can improve the accuracy of mating elements, preventing the occurrence of burrs at the joint. This enhances the quality of repair, ensures the tightness of pipelines, reduces the cost of operation and the period of pipeline recovery. The application of the electric-contact method of processing assembly units, based on the proposed schemes, makes it possible, without sophistication of mobile servicing means, to timely restore the performance of products, eliminating the causes of emergency situations in construction and during operation of pipelines in hydraulic systems of transport machines, especially when used under operation in high-pressure media.

The paper deals with the choice of a method and methodology of planning the technological processes to ensure the required quality of the surface layer of parts, resting on the principle of utility proposed by authors, which is an overall indicator of efficient use of external and internal impacts upon operational characteristics of products with modified surface layers: 1) the modification of properties without allowance removal, with a change in surface layer characteristics; 2) the removal of the allowance portion until reaching the material layer, having the desired operational characteristics; 3) the coating of products with surface layers, made from materials with desired characteristics and having a structure in which the operational properties are obtained by imparting the preset characteristics (wear resistance, heat resistance, etc.) to parts (including those made from other materials), applying the useful properties of coatings deposited; 4) the deposition of coatings comprising the inclusions of conductive and dielectric materials (e.g. ceramics, abrasives, etc.). The paper discovers the ways of using the principle of utility to justify the effectiveness and the scope of preferred application of technological processes of surface layer modification. It presents the methods of automation in selecting the technical means for enhancing the operational properties of high-tech products.