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On increasing the productive time of drilling oil and gas wells using machine learning methods

A.N. Dmitrievsky, A.G. Sboev, N.A. Eremin, A.D. Chernikov, A.V. Naumov, A.V. Gryaznov, I.A. Moloshnikov, S.O. Borozdin, E.A. Safarova

Original article

DOI https://doi.org/10.18599/grs.2020.4.79-85

79-85
rus.
eng.

open access

Under a Creative Commons license
The article is devoted to the development of a hybrid method for predicting and preventing the development of troubles in the process of drilling wells based on machine learning methods and modern neural network models. Troubles during the drilling process, such as filtrate leakoff; gas, oil and water shows and sticking, lead to an increase in unproductive time, i.e. time that is not technically necessary for well construction and is caused by various violations of the production process. Several different approaches have been considered, including based on the regression model for predicting the indicator function, which reflects an approach to a developing trouble, as well as anomaly extraction models built both on basic machine learning algorithms and using the neural network model of deep learning. Showing visualized examples of the work of the developed methods on simulation and real data. Intelligent analysis of Big Geodata from geological and technological measurement stations is based on well-proven machine learning algorithms. Based on these data, a neural network model was proposed to prevent troubles and emergencies during the construction of wells. The use of this method will minimize unproductive drilling time.
 
Machine learning, neural networks, detection of anomalies, prediction of troubles, hybrid simulation, drilling of oil and gas wells, geological and technological information, prevention of accidents and complications, artificial intelligence, automated system, neural network modeling
 
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Anatoliy N. Dmitrievsky
Oil and Gas Research Institute of the Russian Academy of Sciences
3, Gubkin st., Moscow, 119333, Russian Federation

Alexander G. Sboev
National Research Center «Kurchatov Institute»
1, Ak. Kurchatov pl., Moscow, 123098, Russian Federation

Nikolai A. Eremin
Oil and Gas Research Institute of the Russian Academy of Sciences; National University of Oil and Gas «Gubkin University»
3, Gubkin st., Moscow, 119333, Russian Federation

Alexander D. Chernikov
Oil and Gas Research Institute of the Russian Academy of Sciences
3, Gubkin st., Moscow, 119333, Russian Federation

Aleksandr V. Naumov
Oil and Gas Research Institute of the Russian Academy of Sciences
3, Gubkin st., Moscow, 119333, Russian Federation

Artem V. Gryaznov
Oil and Gas Research Institute of the Russian Academy of Sciences
3, Gubkin st., Moscow, 119333, Russian Federation

Ivan A. Moloshnikov
Oil and Gas Research Institute of the Russian Academy of Sciences
3, Gubkin st., Moscow, 119333, Russian Federation

Sergei O. Borozdin
National University of Oil and Gas «Gubkin University»
65, Leninsky ave, Moscow, 119991, Russian Federation

Elizaveta A. Safarova
Oil and Gas Research Institute of the Russian Academy of Sciences
3, Gubkin st., Moscow, 119333, Russian Federation

 

For citation:

Dmitrievsky A.N., Sboev A.G., Eremin N.A., Chernikov A.D., Naumov A.V., Gryaznov A.V., Moloshnikov I.A., Borozdin S.O., Safarova E.A. (2020). On increasing the productive time of drilling oil and gas wells using machine learning methods. Georesursy = Georesources, 22(4), pp. 79–85. DOI: https://doi.org/10.18599/grs.2020.4.79-85