Open Access
Lecture Notes in Computer Science, издание 12742 LNCS, страницы 394-407
Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks
Deeva Irina
1
,
Bubnova Anna
1
,
Andriushchenko Petr
1
,
Voskresenskiy Anton
1, 2
,
Bukhanov Nikita
1, 3
,
2
Gazpromneft-GEO, Saint-Petersburg, Russia
3
Gazpromneft Science and Technology Center, Saint-Petersburg, Russia
Тип публикации: Book Chapter
Дата публикации: 2021-06-10
Springer Nature
Журнал:
Lecture Notes in Computer Science
Квартиль SCImago: Q3
Квартиль WOS: —
Impact factor: —
ISSN: 03029743, 16113349, 18612075, 18612083
Краткое описание
In this paper, a multipurpose Bayesian-based method for data analysis, causal inference and prediction in the sphere of oil and gas reservoir development is considered. This allows analysing parameters of a reservoir, discovery dependencies among parameters (including cause and effects relations), checking for anomalies, prediction of expected values of missing parameters, looking for the closest analogues, and much more. The method is based on extended algorithm MixLearn@BN for structural learning of Bayesian networks. Key ideas of MixLearn@BN are following: (1) learning the network structure on homogeneous data subsets, (2) assigning a part of the structure by an expert, and (3) learning the distribution parameters on mixed data (discrete and continuous). Homogeneous data subsets are identified as various groups of reservoirs with similar features (analogues), where similarity measure may be based on several types of distances. The aim of the described technique of Bayesian network learning is to improve the quality of predictions and causal inference on such networks. Experimental studies prove that the suggested method gives a significant advantage in missing values prediction and anomalies detection accuracy. Moreover, the method was applied to the database of more than a thousand petroleum reservoirs across the globe and allowed to discover novel insights in geological parameters relationships.
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
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Deeva I. et al. Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks // Lecture Notes in Computer Science. 2021. Vol. 12742 LNCS. pp. 394-407.
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Deeva I., Bubnova A., Andriushchenko P., Voskresenskiy A., Bukhanov N., Nikitin N. O., Kalyuzhnaya A. V. Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks // Lecture Notes in Computer Science. 2021. Vol. 12742 LNCS. pp. 394-407.
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TY - GENERIC
DO - 10.1007/978-3-030-77961-0_33
UR - https://doi.org/10.1007/978-3-030-77961-0_33
TI - Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks
T2 - Lecture Notes in Computer Science
AU - Deeva, Irina
AU - Bubnova, Anna
AU - Andriushchenko, Petr
AU - Voskresenskiy, Anton
AU - Bukhanov, Nikita
AU - Nikitin, Nikolay O
AU - Kalyuzhnaya, Anna V
PY - 2021
DA - 2021/06/10 00:00:00
PB - Springer Nature
SP - 394-407
VL - 12742 LNCS
SN - 0302-9743
SN - 1611-3349
SN - 1861-2075
SN - 1861-2083
ER -
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@incollection{Deeva_2021,
doi = {10.1007/978-3-030-77961-0_33},
url = {https://doi.org/10.1007%2F978-3-030-77961-0_33},
year = 2021,
publisher = {Springer International Publishing},
pages = {394--407},
author = {Irina Deeva and Anna Bubnova and Petr Andriushchenko and Anton Voskresenskiy and Nikita Bukhanov and Nikolay O. Nikitin and Anna V. Kalyuzhnaya},
title = {Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks},
booktitle = {Computational Science {\textendash} {ICCS} 2021}
}
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Deeva, Irina, et al. “Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks.” Lecture Notes in Computer Science, 2021, pp. 394–407. Crossref, https://doi.org/10.1007/978-3-030-77961-0_33.