Computers and Geosciences, volume 161, pages 105061
Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea
Publication type: Journal Article
Publication date: 2022-04-01
Journal:
Computers and Geosciences
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 4.4
ISSN: 00983004
Information Systems
Computers in Earth Sciences
Abstract
The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focused on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics for appropriate wells allocation and parametrization, using machine learning methods. For oil production estimation, we implemented and investigated the quality of forecasting models: physics-based, pure data-driven, and hybrid one. The CRMIP model was chosen as a physics-based approach. We compare it with the machine learning and hybrid methods in a frame of oil production forecasting task. In the investigation of reservoir characteristics for wells location choice, we automated the seismic analysis using evolutionary identification of convolutional neural network for the reservoir detection. The Volve oil field dataset was used as a case study to conduct the experiments. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.
Citations by journals
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1
2
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SPE Reservoir Evaluation and Engineering
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SPE Reservoir Evaluation and Engineering, 2, 28.57%
SPE Reservoir Evaluation and Engineering
2 publications, 28.57%
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ISPRS International Journal of Geo-Information
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ISPRS International Journal of Geo-Information
1 publication, 14.29%
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Intelligent Systems with Applications
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Intelligent Systems with Applications
1 publication, 14.29%
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Computers and Geosciences
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Computers and Geosciences
1 publication, 14.29%
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1
2
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Citations by publishers
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1
2
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SPE
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SPE, 2, 28.57%
SPE
2 publications, 28.57%
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Elsevier
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Elsevier
2 publications, 28.57%
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 14.29%
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1
2
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Nikitin N. O. et al. Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea // Computers and Geosciences. 2022. Vol. 161. p. 105061.
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Nikitin N. O., Hvatov A., Vychuzhanin P., Revin I., Kalyuzhnaya A. V., Kalyuzhnaya A. Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea // Computers and Geosciences. 2022. Vol. 161. p. 105061.
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TY - JOUR
DO - 10.1016/j.cageo.2022.105061
UR - https://doi.org/10.1016%2Fj.cageo.2022.105061
TI - Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea
T2 - Computers and Geosciences
AU - Nikitin, Nikolay O
AU - Hvatov, Alexander
AU - Vychuzhanin, Pavel
AU - Revin, Ilia
AU - Kalyuzhnaya, Anna V
AU - Kalyuzhnaya, Anna
PY - 2022
DA - 2022/04/01 00:00:00
PB - Elsevier
SP - 105061
VL - 161
SN - 0098-3004
ER -
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@article{2022_Nikitin,
author = {Nikolay O Nikitin and Alexander Hvatov and Pavel Vychuzhanin and Ilia Revin and Anna V Kalyuzhnaya and Anna Kalyuzhnaya},
title = {Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea},
journal = {Computers and Geosciences},
year = {2022},
volume = {161},
publisher = {Elsevier},
month = {apr},
url = {https://doi.org/10.1016%2Fj.cageo.2022.105061},
pages = {105061},
doi = {10.1016/j.cageo.2022.105061}
}