ISSN 0132-2222 Scientific-technical journal AND COMMUNICATION IN OIL INDUSTRY published since 1973 July 2021 № 7(576) 12 issues per year
CONTENТS |
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INSTRUMENTS OF MEASUREMENT, AUTOMATION, TELEMECHANIZATION AND COMMUNICATION |
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Esaulenko V.N. The tuning-fork sensor of a borehole curvature zenith angle (p. 6‑9) |
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INFORMATION, MEASURING, EXPERT, TRAINING SYSTEMS |
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Gorbunov S.S., Kostandyan А.V., Egorov A.F., Sidorov V.V., Aleksanyan A.A. Intelligent gasoline blending control system in real time taking into account parametric uncertainty (p. 28‑36) |
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MATHEMATICAL MODELING AND SOFTWARE |
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MEMORIAL DATES |
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In memory of Alexander Grigorievich Lachkov (p. 60‑60) |
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Information on the articles |
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UDC 681.5:622.24 DOI: 10.33285/0132-2222-2021-7(576)-6-9
THE TUNING-FORK SENSOR OF A BOREHOLE CURVATURE ZENITH ANGLE (p. 6)
Vladimir Nikolaevich Esaulenko, Dr. of tech. sci., Professor
Astrakhan State Technical University 16, Tatischev str., Astrakhan, 414056, Russian Federation, e-mail: atp@astu.org
The paper notes the necessity of controlling the wellbore spatial position directly during drilling and transmitting information via the wireless electrical communication channel of the well bottom with the well head. The existing means of measuring a borehole curvature zenith angle are considered, their disadvantages are shown. The scheme of the frequency sensor of the zenith angle is given. The design and principle of operation of the sensor are described. The results of the sensor experimental study are presented. The main functional dependence of the tuning fork Q-factor on the zenith angle value of the borehole curvature is revealed. It is shown that the proposed sensor has increased measurement accuracy. Recommendations for eliminating additional error are given.
Keywords: zenith angle; borehole curvature; tuning fork; quality factor; frequency response; bandwidth; natural frequency; drive and vibration pickup system; SOI technology.
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UDC 681.5:622.276 DOI: 10.33285/0132-2222-2021-7(576)-10-16
ON THE POSSIBILITY OF
USING THE RESONATOR METHOD
Igor Nikolaevich Moskalev, Dr. phys.-math. sci., Deputy-Head of the Research Laboratory, Alexander Vyacheslavovich Semenov, Dr. of economic sci., Professor, Rector
S.Yu. Witte Moscow University 12, bld. 1, Vtoroy Kozhukhovsky proezd, Moscow, 115432, Russian Federation, e-mail: igor.moskalev.2015@mail.ru, asemenov@muiv.ru
Yury Ivanovich Orekhov, Dr. of tech. sci., chief researcher
Branch of Russian Federal Nuclear Center-All-Russian Research and Development Institute of Experimental Physics of Sedokov Research and Development Institute Box No 486, Nizhny Novgorod, 603951, Russian Federation, e-mail: orekhov@niiis.nnov.ru
Dmitry Viktorovich Izyumchenko, Cand. of tech. sci., Head of the Center
LLC "Gazprom VNIIGAZ" 15, bld. 1, Proektiruemy proezd № 5537, Razvilka sttl., Vidnoe, 142717, Moscow region, Russian Federation, e-mail: D_izyumchenko@vniigaz.gazprom.ru
To solve the problem of determining the volumetric concentrations of the gas condensate wells production components, a method is used based on the reaction of a microwave resonator to a flow of a gas condensate mixture – gas, water and hydrocarbon condensate passing through it, taking into account the differences in their dielectric constants and the fact that only water absorbs microwave energy of the resonator. When extending the method to the production of oil wells, they also face limitations: oil contains sulfur compounds, which also lead to the absorption of microwave energy. The paper analyzes the losses caused by oil and the areas of application of the method when replacing hydrocarbon condensate with oil.
Keywords: gas-liquid flow; microwave resonator; the dielectric conductivity; measurement error; hydrocarbon condensate; oil; consumption.
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UDC 004.85+89:681.518:658.5.012.7:622.06 DOI: 10.33285/0132-2222-2021-7(576)-17-27
RISK ANALYSIS WHEN
APPLYING ARTIFICIAL INTELLIGENCE TECHNOLOGIES
Anatoly Nikolaevich Dmitrievsky1, 2, Dr. of geological-mineralogical sci., Professor, academician of RAS, Nikolay Alexandrovich Eremin1, 2, Dr. of tech. sci., Professor, Pavel Sergeevich Lozhnikov3, 4, Dr. of tech. sci., Sergey Alexandrovich Klinovenko4, Vladimir Evgenievich Stolyarov1, Daniil Pavlovich Inivatov3
1 Oil and Gas Research Institute Russian Academy of Sciences 3, Gubkin str., Moscow, 119333, Russian Federation, e-mail: a.dmitrievsky@ipng.ru, ermn@mail.ru, bes60@rambler.ru
2 National University of Oil and Gas "Gubkin University" 65, bld. 1, Leninsky prosp., Moscow, 119991, Russian Federation
3 Omsk State Technical University 11, prosp. Mira, Omsk, 644050, Russian Federation, e-mail: daniilini@mail.ru
4 LLC "Gazprom VNIIGAZ" 15, bld. 1, Proektiruemy proezd № 5537, Razvilka sttl., Vidnoe, 142717, Moscow region, Russian Federation, e-mail: P_Lozhnikov@vniigaz.gazprom.ru, S_Klinovenko@vniigaz.gazprom.ru
The paper describes possible risks in the oil and gas industry when using artificial intelligence technologies. The digital economy is becoming a key element of the competitiveness of the Russian fuel and energy complex. The transition from export-raw materials to resource-innovative development is the first stage of the implementation of the digital modernization strategy. The oil and gas complex currently has the world's largest mineral resource base, developed infrastructure, qualified personnel and significant innovation potential, including the ability to implement digital technologies and high-conversion industries, which implies a large-scale, fast and efficient return on invested financial resources, new business models to maintain the leading positions of hydrocarbons production in the long term. These capabilities can only be achieved through the introduction of technologies based on the acquisition and processing of large amounts of data, machine learning and digital twins in order to minimize uncertainty factors and risk assessment, as well as to prevent possible abnormal situations and minimize damage caused by technological regimes violation. Uncertainty factors can make it difficult to predict the financial, social, logistical and other business conditions, therefore, long-term major projects may not be implemented. The paper also considers the reasons for the current conservative policy on the innovative products promotion in the oil and gas industry as well as the integration of digital and technological decision-makings that improve the efficiency of management of production facilities of the production infrastructure. The main possible risks and the degree of their risk impact on the project are presented. Possible measures to reduce risks and their consequences are presented on the example of creating a high-performance intelligent system for preventing drilling of oil and gas wells, which allows creating a basis for the transition to unpopulated technologies for preventing accidents and remote work in oil and gas fields.
Keywords: artificial intelligence; system; risk; damage; intelligent technologies; big data; drilling; oil and gas production.
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UDC 004.942+665.73 DOI: 10.33285/0132-2222-2021-7(576)-28-36
INTELLIGENT GASOLINE BLENDING
CONTROL SYSTEM IN REAL TIME
Sergey Sergeevich Gorbunov, First Deputy Director, Ashot Arsenovich Aleksanyan, leading specialist
LLC "MCE-Engineering" 5A, bld. 1, 9th floor, apart. 1, Beregovoy proezd, Moscow, 121087, Russian Federation, e-mail: gorbunov@mcee.ru, ash410@mail.ru
Artur Valerievich Kostandyan, Director
LLC "KSIMATIC" 12, bld. 1, apart. 50, Turistskaya str., Moscow, 125459, Russian Federation, e-mail: avkost77@gmail.com
Aleksandr Fedorovich Egorov, Dr. of tech. sci., Professor, Head of the Department
Mendeleev University of Chemical Technology of Russia 20, Geroev Panfilovtstev str., Moscow, 125480, Russian Federation, e-mail: egorov@muctr.ru
Valery Vasilievich Sidorov, Cand. of tech. sci., Professor, Head of the Department
National University of Oil and Gas "Gubkin University" 65, bld. 1, Leninsky prosp., Moscow, 119991, Russian Federation, e-mail: sidorov.v.v@gubkin.ru
The process of gasoline blending is an important final stage in the overall technological chain of gasoline production. The technological costs of compounding are determined by the efficiency of maintaining the hydrodynamic mode of mixing gasoline components, control, optimization of the fuels mixing components, optimal control and regulation. The development of a mathematical model, the formulation of the problem of optimization and control over gasoline mixing in real time (online mode) is one of the important stages in the creation of a distributed control system (DCS). The paper presents a mathematical model of gasolines mixing online optimization, taking into account the conditions of uncertainty of the technological regime parameters. The presented mathematical model takes into account the parametric uncertainty of the gasoline mixing process in real time when constructing control algorithms in the DCS. A neural network model for solving the problem of forecasting and optimizing a gasoline blending control system in real time is presented.
Keywords: gasoline mixing; intelligent system; optimization model; gasoline mixing recipe; mathematical description; optimality criterion; limitation; parametric uncertainty; neural network.
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UDC 681.5:519.86 DOI: 10.33285/0132-2222-2021-7(576)-37-46
SYSTEM MODELING AND RISKS
ASSESSMENT AND OPTIMIZATION
Alexey Vyacheslavovich Kryuchkov, Cand. of tech. sci., associate professor, Yury Petrovich Stepin, Dr. of tech. sci., Professor
National University of Oil and Gas "Gubkin University" 65, bld. 1, Leninsky prosp., Moscow, 119991, Russian Federation, e-mail: hook66@list.ru, stepin.y@gubkin.ru
The paper is devoted to solving the problems of system modeling based on the method of dynamics of averages of the theory of the Markov random processes, the functioning (operation) of the service for the synthesis of special software (SSS) of large automated control systems (ACS), including automated control systems for oil and gas enterprises, in conditions of uncertainty and risk. The operation of the SSS synthesis service is considered at the stable operation stage – the stationary operation of the Markov chain. A multi-criteria dispersion model for assessing the risk of their functioning is proposed using the average numbers of the objects states (elements of the systems under consideration) and the intensities of their transitions from state to state. The models of efficiency and risk assessment for selecting the optimal strategy (functioning option) are proposed according to the selected set of formed compromise criteria and restrictions.
Keywords: transition intensity; average state size; multi-criteria assessment; risk; operation strategy; medium dynamics; the Markov chain stationary mode; special software; remote software development service.
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UDC 681.5:622.276 DOI: 10.33285/0132-2222-2021-7(576)-47-56
FACTORIZATION OF FLOW
PROFILE DATA IN PRODUCTION AND INJECTION WELLS
Konstantin Anatolievich Sidelnikov, Cand. of tech. sci., chief specialist
CJSC "Izhevsk Petroleum Scientific Center" 175, Svoboda str., Izhevsk, 426057, Russian Federation, e-mail: sidelkin@yandex.ru
Rinat Vasilovich Faizullin, Cand. of economic sci., associate professor
MIREA – Russian Technological University 78, Vernadsky prosp., Moscow, 119454, Russian Federation, e-mail: rf85@mail.ru
Using the results of well tests, the problem of factorization of flow rate profiles data of production and injection wells completed in a two-layered reservoir is formulated. This problem is ill-posed, and the process of adding information in order to solve it is required (regularization). Several methods of deterministic regularization based on 𝓁2-norm minimization of the unknown vector are considered. It is shown that such approaches do not take into account the petrophysical properties of the reservoir and cannot cover all possible factorization combinations. The Bayesian regularization is proposed to factorize the flow profile data. According to this method, all relative factors are defined by the corresponding probability distribution functions. Core studies are used to determine the joint probable distribution of rock permeability and porosity. Layer productivity ratio distributions are calculated separately for each well based on its log interpretation data. The Bayesian statistical inference is used to obtain the general drawdown ratio distribution for the entire field. This approach was tested on real data obtained from three fields.
Keywords: regularization; the Bayes’ theorem; statistical inference; well test; flow profile; productivity index; injectivity index.
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UDC 531:622.276+622.269 DOI: 10.33285/0132-2222-2021-7(576)-57-59
SIMULATION OF CAVITATION CORROSION IN PIPELINES (p. 57)
Ilgiz Ikhsanovich Gallyamov, Dr. of tech. sci., Professor, Ruslan Albertovich Gilyazetdino, Ruzil Fandasovich Mardanov, Lilya Fanovna Yusupova, lecturer
Branch of Ufa State Petroleum Technological University in the city of Oktyabrsky 54а, Devonskaya str., Oktyabrsky, 452607, Republic of Bashkortostan, Russian Federation, e-mail: ilgiz.gallyamov@inbox.ru, strong.gilyazetdinov@mail.ru, mardanov.ruzil@mail.ru, shalilya@yandex.ru
Cavitation corrosion in pipelines is a common type of metal surface destruction. To understand the processes of parts wear, it is necessary to apply methods based on computer modeling. The cases considered in this paper take place in external and underground oil pipelines.
Keywords: cavitation corrosion; ANSYS-software; cathodic protection; simulation; pipe inner surface; oil pipeline operation.
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IN MEMORY OF ALEXANDER GRIGORIEVICH
LACHKOV
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NATIONAL UNIVERSITY OF OIL AND GAS "GUBKIN UNIVERSITY" |