ENSURING THE QUALITY AND VOLUME OF GEOLOGICAL-TECHNOLOGICAL DATA FOR APPLICATION OF MACHINE LEARNING METHODS KNOWLEDGE-ORIENTED SYSTEM
DOI:
https://doi.org/10.31471/1993-9981-2021-1(46)-75-92Keywords:
geological and technical data, complications, machine learning, knowledge-oriented system, drilling rigs.Abstract
The article considers the issues of complications arising during the technological processes of drilling related to geological, geophysical and external conditions, urbanization and detection of inconsistencies between the actual drilling conditions from the projected ones due to changes in climate and geological changes, which occur in the period from the end of the project and the actual start of development of the field. The interrelation of the factors that complicate the drilling process with the stages of design and organization of precedents, on the basis of which the technological processes of drilling are modeled, is analyzed. The phases of the cycle of reasoning based on knowledge with a projection on the information cycle of drilling process control are revealed. The architecture of automation of technological processes of drilling with reference to the pyramid of computer-integrated production is presented. The article shows the possibility of applying machine learning methods to data analysis tasks related to the drilling process. The use of a combined approach to adapt the data used for knowledge-based decision-making is proposed. The problem of insufficient precedent for training knowledge-oriented system of intellectual support of decision-making processes is considered and the provision of full-scale simulators with the necessary amount of data for modeling complications of high-risk drilling process is substantiated. The authors determine the level of the expected ratio between the main objects of the knowledge-oriented system of intelligent decision-making on the course of the technological process: the problems of machine learning on the one hand and oil and gas wells on the other.
Downloads
References
Yeremin N.A., Arkhipov A.I. Sardanashvili O. N. Stolyarov V. Ye. Tsifrovye tekhnologii stroitelstva skvazhin Digital well-building technologie Delovoy zhurnal Neftegaz.RU, № 4,2020 (in Russ.)
Dmitrievskiy A.N., Yeremin N.A., Stolyarov V.Ye. (2020b). Rol informatsii v primenenii tekhnologiy iskusstvennogo intellekta pri stroitelstve skvazhin dlya neftegazovykh mestorozhdeniy. Nauchnyy zhurnal Rossiyskogo gazovogo obshchestva, 3(26), s. 22–37(in Russ.)
M.M. Khasanov, D.O. Prokofev, O.S. Ushmaev, B.V. Belozerov, R.R. Gilmanov, A.S. Margarit Perspektivnye tekhnologii Big Data v neftyanom inzhiniringe: opyt kompanii «Gazprom neft» / // Neftyanoe khozyaystvo, 2016, № 12. C. 76 – 79(in Russ.)
Stolyarov V.E., Yeremin N.A., Yeremin A.N., Basnieva I.K.. Tsifrovye gazovye skvazhiny: sostoyanie i perspektivy // Neftepromyslovoe delo», 2018 g., № 7, s. 48 – 55, DOI: 10.30713/0207- 2351-2018-7-48-55. (in Russ.)
Hobyr L.M. Ymovirnisna otsinka rezultativ interpretatsii danykh ta parametriv heofizychnykh doslidzhen / Hobyr L.M., Vovk R.B., Poteriailo L.O., Sheketa V.I. // Vseukrainskyi shchokvartalnyi naukovo-tekhnichnyi zhurnal “Rozvidka ta rozrobka naftovykh i hazovykh rodovyshch”. 2018;3(68).p. 46-59(in Ukr.).
Dmitrievskiy A.N., Duplyakin V.O., Yeremin N.A., Kapranov V.V. Neyrosetevoe modelirovanie v sistemakh preduprezhdeniya oslozhneniy i avariynykh situatsiy pri stroitelstve neftyanykh i gazovykh skvazhin //Davachі i sistemy.№12.2019 g., s.21 – 27(in Russ.)
Razrabotka protsessa prinyatiya resheniy pri modelirovanii i proektirovanii mestorozhdeniy uglevodorodov na osnove vyvoda po pretsedentam. Novye informatsionnye tekhnologii v neftegazovoy otrasli i obrazovanii: materialy VIII Mezhdunarodnoy nauchno-tekhnicheskoy konferentsii; otv. red. O. N. Kuzyakov. – Tyumen: TIU, 2019. – 324 s. – Tekst: neposredstvennyy. ISBN 978-5-9961-2225-7 . (in Russ.)
Kabyshev B.P. Geologiya i neftegazonosnost Dneprovo-Donetskoy zapadiny. Neftegazonosnost / B.P. Kabyshev, P.F. Shpak, O.D. Bilyk i dr. – Kiev: Naukova dumka, 1989. – 204 s. (in Russ.)
Funktsionalnye vozmozhnosti polnomasshtabnogo burovogo trenazhera DrillSim-5000 [Yelektronniy resurs] – Rezhim dostupu do resursu: https://www.gubkin.ru/faculty/ oil_and_gas_development/chairs_and_departments/drill_center/DrillSim-5000.php. (in Russ.)
Krygina A.S, Ivanov D.B., Sharafieva Z.F. Aktualnye problemy ekologii dobychi, transporta i pererabotki nefti. materialy VII Mezhdunarodnoy konferentsii s elementami nauchnoy shkoly dlya molodezhi. 2018s 179(in Russ.)
Yeremin A.N. Novaya klassifikatsiya tsifrovykh i intellektualnykh skvazhin // Avtomatizatsiya i IT v neftegazovoy oblasti, № 2 (24) 3, 2016 g., s. 2 – 4(in Russ.)
Chernikov A.D., Eremin N.A., Stolyarov V.E., Sboev A.G., Semenova-Chashchina O.K., Fitsner L.K. (2020). Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells: problems and solutions. Georesursy = Georesources, 22(3),pp.87–96. DOI: https://doi.org/10.18599/grs.2020.3.87-96
Poteriailo L.O. Vykorystannia CBR pry realizatsii inzhenernykh rishen system klasu «Burovi trenazhery» /Poteriailo L.O., Protsiuk V.V.,. Kravtsiv K.I//VI Mizhnar. nauk.-tekhn. konf. «Kompiuterne modeliuvannia ta optymizatsiia skladnykh system», Dnipro, 4.11.2020. – Dnipro, 2020. (in Ukr.)
Poteriailo L.O. Modeliuvannia imitatsionnoi modeli keruvannia protsesamy burinnia na osnovi pretsedentiv /Poteriailo L.O., Protsiuk V.V.,. Kravtsiv K.I// Vseukrainskoi nauk.-prakt. konf. «Informatsiini tekhnolohii v osviti, tekhnitsi ta promyslovosti » - ITOTP-2020 -Ivano-Frankivsk, 8 zhovtnia 2020. (in Ukr.)
.png)
