ANN based interwell connectivity analysis in cyber-physical petroleum systems

Haibo Cheng; Xiaoning Han; Peng Zeng; Haibin Yu; Evgeny Osipov; Valeriy Vyatkin

doi:10.1109/INDIN41052.2019.8972285

ANN based interwell connectivity analysis in cyber-physical petroleum systems

Haibo Cheng
, Xiaoning Han
, Peng Zeng
, Haibin Yu
, Evgeny Osipov
, Valeriy Vyatkin

Chinese Academy of Sciences
Luleå University of Technology
University of CAS

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

5 Sitaatiot (Scopus)

Abstrakti

In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (OG) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.

Alkuperäiskieli	Englanti
Otsikko	Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019
Kustantaja	IEEE
Sivut	199-205
Sivumäärä	7
ISBN (elektroninen)	9781728129273
DOI - pysyväislinkit	https://doi.org/10.1109/INDIN41052.2019.8972285
Tila	Julkaistu - 1 heinäk. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Industrial Informatics - Aalto University, Helsinki and Espoo, Suomi Kesto: 22 heinäk. 2019 → 25 heinäk. 2019 Konferenssinumero: 17 https://www.indin2019.org/

Julkaisusarja

Nimi	IEEE International Conference on Industrial Informatics
Kustantaja	IEEE
ISSN (painettu)	1935-4576
ISSN (elektroninen)	2378-363X

Conference

Conference	IEEE International Conference on Industrial Informatics
Lyhennettä	INDIN
Maa/Alue	Suomi
Kaupunki	Helsinki and Espoo
Ajanjakso	22/07/2019 → 25/07/2019
www-osoite	https://www.indin2019.org/

Rahoitus

ACKNOWLEDGMENT This work is supported in part by the Natural Science Foundation of China under Grant 61533015, and in part by the Joint PhD Training Program Scholarship of the University of Chinese Academy of Sciences (UCAS). The support is gratefully acknowledged.

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10.1109/INDIN41052.2019.8972285

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Cheng, H., Han, X., Zeng, P., Yu, H., Osipov, E., & Vyatkin, V. (2019). ANN based interwell connectivity analysis in cyber-physical petroleum systems. teoksessa Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019 (Sivut 199-205). Artikkeli 8972285 (IEEE International Conference on Industrial Informatics). IEEE. https://doi.org/10.1109/INDIN41052.2019.8972285

@inproceedings{f496342d7f7347fb9781d7c14d63828f,

title = "ANN based interwell connectivity analysis in cyber-physical petroleum systems",

abstract = "In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (OG) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.",

keywords = "Artificial neural network (ANN), Cyber-physical petroleum systems(CPPS), Interwell connectivity, Long short-term memory (LSTM), Waterflooded reservoir",

author = "Haibo Cheng and Xiaoning Han and Peng Zeng and Haibin Yu and Evgeny Osipov and Valeriy Vyatkin",

year = "2019",

month = jul,

day = "1",

doi = "10.1109/INDIN41052.2019.8972285",

language = "English",

series = "IEEE International Conference on Industrial Informatics",

publisher = "IEEE",

pages = "199--205",

booktitle = "Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019",

address = "United States",

note = "IEEE International Conference on Industrial Informatics, INDIN ; Conference date: 22-07-2019 Through 25-07-2019",

url = "https://www.indin2019.org/",

}

Cheng, H, Han, X, Zeng, P, Yu, H, Osipov, E & Vyatkin, V 2019, ANN based interwell connectivity analysis in cyber-physical petroleum systems. julkaisussa Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019., 8972285, IEEE International Conference on Industrial Informatics, IEEE, Sivut 199-205, IEEE International Conference on Industrial Informatics, Helsinki and Espoo, Suomi, 22/07/2019. https://doi.org/10.1109/INDIN41052.2019.8972285

ANN based interwell connectivity analysis in cyber-physical petroleum systems. / Cheng, Haibo; Han, Xiaoning; Zeng, Peng et al.
Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019. IEEE, 2019. s. 199-205 8972285 (IEEE International Conference on Industrial Informatics).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - ANN based interwell connectivity analysis in cyber-physical petroleum systems

AU - Cheng, Haibo

AU - Han, Xiaoning

AU - Zeng, Peng

AU - Yu, Haibin

AU - Osipov, Evgeny

AU - Vyatkin, Valeriy

N1 - Conference code: 17

PY - 2019/7/1

Y1 - 2019/7/1

N2 - In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (OG) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.

AB - In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (OG) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.

KW - Artificial neural network (ANN)

KW - Cyber-physical petroleum systems(CPPS)

KW - Interwell connectivity

KW - Long short-term memory (LSTM)

KW - Waterflooded reservoir

UR - https://www.scopus.com/pages/publications/85079078685

U2 - 10.1109/INDIN41052.2019.8972285

DO - 10.1109/INDIN41052.2019.8972285

M3 - Conference article in proceedings

AN - SCOPUS:85079078685

T3 - IEEE International Conference on Industrial Informatics

SP - 199

EP - 205

BT - Proceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019

PB - IEEE

T2 - IEEE International Conference on Industrial Informatics

Y2 - 22 July 2019 through 25 July 2019

ER -

ANN based interwell connectivity analysis in cyber-physical petroleum systems

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