Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies

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Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies. / Abdurakipov, S.

In: Journal of Physics: Conference Series, Vol. 2119, No. 1, 012109, 15.12.2021.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Abdurakipov, S 2021, 'Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies', Journal of Physics: Conference Series, vol. 2119, no. 1, 012109. https://doi.org/10.1088/1742-6596/2119/1/012109

APA

Abdurakipov, S. (2021). Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies. Journal of Physics: Conference Series, 2119(1), [012109]. https://doi.org/10.1088/1742-6596/2119/1/012109

Vancouver

Abdurakipov S. Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies. Journal of Physics: Conference Series. 2021 Dec 15;2119(1):012109. doi: 10.1088/1742-6596/2119/1/012109

Author

Abdurakipov, S. / Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies. In: Journal of Physics: Conference Series. 2021 ; Vol. 2119, No. 1.

BibTeX

@article{a72f29ed02424020b3180d7b5c3829c6,

title = "Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies",

abstract = "The current coverage of oil wells with telemetry does not allow timely determination of deviations in the operation of about 40% of electric submersible pumps. To solve this problem, a model of virtual sensors has been developed that allows the prediction of temperature and pressure growth at the pump intake in the absence of submersible sensors based on modern big data processing and machine learning technologies. The developed models of virtual sensors are embedded directly into the process control system, which allows notifying the technologists and operators about a possible reduction in the planned average pump operating time and their possible failures for various reasons.",

author = "S. Abdurakipov",

note = "Publisher Copyright: {\textcopyright} 2021 Institute of Physics Publishing. All rights reserved.; 37th Siberian Thermophysical Seminar, STS 2021 ; Conference date: 14-09-2021 Through 16-09-2021",

year = "2021",

month = dec,

day = "15",

doi = "10.1088/1742-6596/2119/1/012109",

language = "English",

volume = "2119",

journal = "Journal of Physics: Conference Series",

issn = "1742-6588",

publisher = "IOP Publishing Ltd.",

number = "1",

}

RIS

TY - JOUR

T1 - Increasing the efficiency of electric submersible pumps by using big data processing and machine learning technologies

AU - Abdurakipov, S.

PY - 2021/12/15

Y1 - 2021/12/15

N2 - The current coverage of oil wells with telemetry does not allow timely determination of deviations in the operation of about 40% of electric submersible pumps. To solve this problem, a model of virtual sensors has been developed that allows the prediction of temperature and pressure growth at the pump intake in the absence of submersible sensors based on modern big data processing and machine learning technologies. The developed models of virtual sensors are embedded directly into the process control system, which allows notifying the technologists and operators about a possible reduction in the planned average pump operating time and their possible failures for various reasons.

AB - The current coverage of oil wells with telemetry does not allow timely determination of deviations in the operation of about 40% of electric submersible pumps. To solve this problem, a model of virtual sensors has been developed that allows the prediction of temperature and pressure growth at the pump intake in the absence of submersible sensors based on modern big data processing and machine learning technologies. The developed models of virtual sensors are embedded directly into the process control system, which allows notifying the technologists and operators about a possible reduction in the planned average pump operating time and their possible failures for various reasons.

UR - http://www.scopus.com/inward/record.url?scp=85123619488&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/2119/1/012109

DO - 10.1088/1742-6596/2119/1/012109

M3 - Conference article

AN - SCOPUS:85123619488

VL - 2119

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012109

T2 - 37th Siberian Thermophysical Seminar, STS 2021

Y2 - 14 September 2021 through 16 September 2021

ER -

ID: 35609819