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Maksim Simonov
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Proceedings Papers
Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Annual Caspian Technical Conference, October 16–18, 2019
Paper Number: SPE-198411-MS
Proceedings Papers
Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, September 17–19, 2019
Paper Number: SPE-196639-MS
Proceedings Papers
Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Russian Petroleum Technology Conference, October 15–17, 2018
Paper Number: SPE-191592-18RPTC-MS
Proceedings Papers
Nikolay Teplyakov, Andrey Slabetskiy, Nikolay Sarapulov, Maksim Simonov, Dmitriy Perets, Viktor Kotezhekov, Alla Andrianova
Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Russian Petroleum Technology Conference, October 15–17, 2018
Paper Number: SPE-191585-18RPTC-MS
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Options for the article titled, Application of Machine Learning Methods for Modeling the Current Indicators of Operating Wells Stock of PJSC Gazprom Neft
- Download the English language PDF for the article titled, Application of Machine Learning Methods for Modeling the Current Indicators of Operating Wells Stock of PJSC Gazprom Neft
- Download the Russian language PDF for the article titled, Application of Machine Learning Methods for Modeling the Current Indicators of Operating Wells Stock of PJSC Gazprom Neft
Proceedings Papers
Maksim Simonov, Alexey Akhmetov, Pavel Temirchev, Dmitry Koroteev, Ruslan Kostoev, Evgeny Burnaev, Ivan Oseledets
Publisher: Society of Petroleum Engineers (SPE)
Paper presented at the SPE Russian Petroleum Technology Conference, October 15–17, 2018
Paper Number: SPE-191593-18RPTC-MS
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Options for the article titled, Application of Machine Learning Technologies for Rapid 3D Modelling of Inflow to the Well in the Development System
- Download the English language PDF for the article titled, Application of Machine Learning Technologies for Rapid 3D Modelling of Inflow to the Well in the Development System
- Download the Russian language PDF for the article titled, Application of Machine Learning Technologies for Rapid 3D Modelling of Inflow to the Well in the Development System
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