Journal V.24.No.1.2022
Pages
Download article

Oil production management based on neural network optimization of well operation at the pilot project site of the Vatyeganskoe field (Territorial Production Enterprise Povkhneftegaz)

L.S. Brilliant, M.R. Dulkarnaev, M.Yu. Danko, A.O. Elisheva, D.Kh. Nabiev, A.I. Khutornaya, I.N. Malkov

Original article

DOI https://doi.org/10.18599/grs.2022.1.1

3-15
rus.

open access

Under a Creative Commons license

Optimization of the “mature” fields development in machine learning algorithms is one of the urgent problems nowadays. The task is set to extend the effective operation of wells, optimize production management at the late stage of field development. Based on the task set, the article provides an overview of possible solutions in waterflooding management problems. Production management technology is considered as an alternative to intensification of operation, which is associated with an increase in the produciton rate and involves finding solutions aimed at reducing the water cut of well production. The practical implementation of the “Neural technologies for production improvement” includes the following steps: evaluation, selection, predictive analytics. The result is a digital technological regime of wells that corresponds to the set goal and the solution of the optimization problem in artificial intelligence algorithms using the software and hardware complex “Atlas – Waterflood Management”.

“Neural technologies for production improvement” have been successfully tested at the pilot project site of the productive formation of the Vatyeganskoe field. The article provides a thorough and detailed analysis of the work performed, describes the algorithms and calculation results of the proxy model using the example of the pilot area, as well as the integration of the “Atlas – Waterflood Management” and the organization of the workflow with the field professionals of the Territorial Production Enterprise Povkhneftegaz.
 

 

field development, neural network optimization, technological regime, machine learning, optimization problem, flood control, oil production, oil production management

 

  • Albertoni, Alejandro & Lake, Larry (2003). Inferring Interwell Connectivity Only From Well-Rate Fluctuations in Waterfloods. SPE Reservoir Evaluation & Engineering, 6, pp. 6–16. https://doi.org/10.2118/83381-PA
  • Arefiev S.V., Yunusov R.R., Valeev A.S., Kornienko A.N., Dulkarnaev M.R., Labutin D.V., Brilliant L.S., Pecherkin M.F., Kokorin D.A., Grandov D.V., Komyagin A.I. (2017). Methodical foundations and experience in the implementation of digital technologies for operational planning and management of the operating modes of production and injection wells in the OPR area of the Yuv1 reservoir of the Vatjeganskoye deposit of the Povkhneftegaz TPP (OOO Lukoil-Western Siberia). Nedropolzovanie XXI vek, 6(69), pp. 60–81. (In Russ.)
  • Brilliant L.S. (2018). Digital Solutions for Production Management at Mature Oil Fields. Neft. Gaz. Novatsii, 4, pp. 61–64. (In Russ.)
  • Brilliant L.S., Dulkarnaev M.R., Danko M.Yu., Elisheva A.O., Tsinkevich O.V. (2020). Challenges of efficient brownfield development: architecture of digital solutions in control of well operation conditions. Nedropolzovanie XXI vek, 4(87), pp. 98–107. (In Russ.)
  • Brilliant L.S., Komyagin A.I., Blyashuk M.M., Tsinkevich O.V., Zhuravleva A.A. (2017). The method of operational control of waterflooding. Patent RF 2614338; publ. 24.03.2017. 
  • Brilliant L.S., Pecherkin M.F., Blyashuk M.M., Tsinkevich O.V., Alekseev A.S. (2019). Development of Practical Solutions for Water Flood Control Problems Based on Neural Network Optimization of Injection Wells Operating Modes. Nedropolzovanie XXI vek, 4(80), pp. 114–123. (In Russ.)
  • Brilliant L.S., Smirnov I.A., Komyagin A.I., Potryasov A.V., Pechorkin M.F., Baryshnikov A.V. (2015). The method of operational control of waterflooding. Patent RF 2565313; publ. 20.10.2015.  
  • Brilliant L.S., Zaviyalov A.S., Danko M.Yu. (2020). The method of operational control of waterflooding. Patent RF 2715593; publ. 02.03.2020. 
  • Gopa, Konstantin, Yamov, Sergey, Naugolnov, Mihail, Perets, Dmitrii, and Maksim Simonov (2018). Cognitive Analytical System Based on Data-Driven Approach for Mature Reservoir Management. Paper presented at the SPE Russian Petroleum Technology Conference. Moscow. https://doi.org/10.2118/191592-18RPTC-MS
  • Guo, Zhenyu, Reynolds, Albert C., & Zhao, Hui (2018). Waterflooding optimization with the INSIM-FT data-driven model. Computational Geosciences (Dordrecht Online), 22(3), pp. 745–761. http://dx.doi.org/10.1007/s10596-018-9723-y
  • Meerov M.V., Litvak B.L. (1972). Optimization of Multiconnected Control Systems. Moscow: Nauka, 344 p. (In Russ.)
  • Mikhaylov V.N., Volkov Yu.A., Dulkarnaev M.R. (2011). Iterative technique of geological hydrodynamic modeling for the estimation of residual oil reserves distribution and planning of geological and technological works. Georesursy, 3(39), pp. 43–48. (In Russ.)
  • Mikhaylov B.N., Dulkarnaev M.R., Volkov Yu.A. (2012). Problems and experience in development design of long-term exploited oil deposits on the example of the Vatyeganskoe field in Western Siberia. Proc. Conf.: High-viscosity oil and natural bitumen: problems and improving the efficiency of exploration and field development. Kazan: Fen, pp. 255–257. (In Russ.)
  • Naugolnov, Mikhail, and Rustam Murtazin (2019). Reservoir Value-Engineering for West Siberian Oil Fields. Paper presented at the SPE Annual Caspian Technical Conference. Baku. https://doi.org/10.2118/198374-MS
  • Naugolnov, Mikhail, Teplyakov, Nikolay, and Maxim Bolshakov (2018). Cost-Engineering Waterflooding Management Methods. Paper presented at the SPE Russian Petroleum Technology Conference. Moscow. https://doi.org/10.2118/191580-18RPTC-MS
  • Nekhoroshkova A.A., Danko M.Yu., Zavyalov A.C., Elisheva A.O. (2019). Critical analysis of the proxy modeling method INSIM-FT (Interwell Numerical SimulationFront Tracking models) on synthetic models and a real field. Neft. Gaz. Novatsii, 12(229), pp. 49–55. (In Russ.)
  • Potryasov A.A., Brilliant L.S., Pecherkin M.F., Komyagin A.I. (2016). Automation of waterflooding control processes in an oil field.. Nedropolzovanie XXI vek, 6(63), pp. 112–121. (In Russ.)
  • Ruchkin A.A, Stepanov S.V, Knyazev A.V, Stepanov A.V, Korytov A.V, Avsyanko I.N. (2018). Applying CRM Model to Study Well Interference. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, 4, pp. 148–168. (In Russ.)
  • Stepanov S.V., Sokolov S.V., Ruchkin A.A., Stepanov A.V., Knyazev A.V., Korytov A.V. (2018). Considerations on Mathematical Modeling of Producer-Injector Interference. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, 3, pp. 146–164. (In Russ.)
     
  •  

Leonid S. Brilliant – PhD (Engineering), Director General
Tyumen Oil & Gas Institute
64, Gertsen st., Tyumen, 625000, Russian Federation

Marat R. Dulkarnaev – PhD (Engineering), Deputy Director General for Field Development – Chief Geologist
ООО LUKOIL-Zapadnaya Sibir TPE Povkhneftegaz
20, Pribaltiyskaya st., Kogalym, 628484, Russian Federation

Mikhail Yu. Danko – Deputy Director General for Science
Tyumen Oil & Gas Institute
64, Gertsen st., Tyumen, 625000, Russian Federation

Aleksandra O. Elisheva – Director of the Department of Analysis and Design of Oil and Gas Field Development 
Tyumen Oil & Gas Institute
64, Gertsen st., Tyumen, 625000, Russian Federation
e-mail: ElishevaAO@togi.ru

Dinar K. Nabiev – Head of the Laboratory
Tyumen Oil & Gas Institute
64, Gertsen st., Tyumen, 625000, Russian Federation

Anastasiya I. Khutornaya – Leading Engineer
Tyumen Oil & Gas Institute
64, Gertsen st., Tyumen, 625000, Russian Federation

Ivan N. Malkov – Engineer
Tyumen Oil & Gas Institute
64, Gertsen st., Tyumen, 625000, Russian Federation
 

For citation:

Brilliant L.S., Dulkarnaev M.R., Danko M.Yu., Elisheva A.O., Nabiev D.Kh., Khutornaya A.I., Malkov I.N. (2022). Oil production management based on neural network optimization of well operation at the pilot project site of the Vatyeganskoe field (Territorial Production Enterprise Povkhneftegaz). Georesursy = Georesources, 24(1), pp. 3–15. DOI: https://doi.org/10.18599/grs.2022.1.1 

© 2023 The Authors. Published by Georesursy LLC