System-integrative approach to automation of the oil and gas fields design and development control

Authors: A.M. Sagdatullin, A.A. Emekeev (Almetyevsk State Oil Institute, RF, Moscow), Е.А. Myraveva (Ufa State Oil Technical University, RF, Sterlitamak)

Key words: geologic-technological model, artificial intelligence, wells process optimization.

At present, the important issue is to develop a system of effective management and monitoring of the oil and gas field, for which have been used hydrodynamic model. In this paper, the basic steps of creating a permanent geological and technological model (PDGTM) and the analysis of the data needed for its construction are presented. On the basis of PDGM and neural network models to improve the system of automatic control and monitoring of oil and gas development is proposed the system-integrated approach that takes into account the basic system principles of the oil and gas development design and control processes. To ensure matching of recovery rates and injection fluid it is proposed local automation and control system of oil production well operation modes. On this basis, the optimization solutions modeling method is functionally extended, providing options to optimize modeling and decision making in the control and management of oil and gas development system.

References
1. RD 153-39-047-00, Reglament po sozdaniyu postoyanno deystvuyushchikh geologo-tekhnologicheskikh modeley neftyanykh i gazoneftyanykh mestorozhdeniy (Regulation on creation of permanent geological and technological models of oil and gas fields), Moscow: Publ. of Ministry of Fuel and Energy of the Russian Federation, 2000, 129 p.
2. Nasybullin A.V., Antonov O.G., Shutov A.A. et al., 3D reservoir modeling and AI-based optimization of waterflooding system (In Russ.), Neftyanoe
khozyaystvo = Oil Industry, 2012, no. 7, pp. 14–16.
3. Ivanenko B.P., Prokazov S.A., Parfenov A.N., Simulation of the oil production processes using neural networks (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2003, no. 12, pp. 46–49.
4. Rozhkin M.E., Mikliia O.A., Possibilities of modelling work of the well equipment at experimental stands (In Russ.), Avtomatizatsiya, telemekhanizatsiya i svyaz' v neftyanoy promyshlennosti, 2009, no. 8, pp. 16–20.
5. Il'yasov B.G., Tagirova K.F., Dunaev I.V., Automation of oilfield equipment diagnostics using neural networks (In Russ.), Avtomatizatsiya, telemekhanizatsiya i svyaz' v neftyanoy promyshlennosti, 2005, no. 4, pp. 11–18.
6. Tagirova K.F., Avtomatizatsiya upravleniya tekhnologicheskim protsessom
dobychi nefti iz malodebitnykh skvazhin na osnove dinamicheskikh modeley
(Automation of the process control of oil production from low production
wells based on dynamic models): Thesis of candidate of technical science,
Ufa, 2008.
7. Gorban' A.N., Rossiev D.A., Neyronnye seti na personal'nom komp'yutere
(Neural network on the personal computer), Novosibirsk: Nauka Publ., 1996, 276 p. 

Key words: geologic-technological model, artificial intelligence, wells process optimization.

At present, the important issue is to develop a system of effective management and monitoring of the oil and gas field, for which have been used hydrodynamic model. In this paper, the basic steps of creating a permanent geological and technological model (PDGTM) and the analysis of the data needed for its construction are presented. On the basis of PDGM and neural network models to improve the system of automatic control and monitoring of oil and gas development is proposed the system-integrated approach that takes into account the basic system principles of the oil and gas development design and control processes. To ensure matching of recovery rates and injection fluid it is proposed local automation and control system of oil production well operation modes. On this basis, the optimization solutions modeling method is functionally extended, providing options to optimize modeling and decision making in the control and management of oil and gas development system.

References
1. RD 153-39-047-00, Reglament po sozdaniyu postoyanno deystvuyushchikh geologo-tekhnologicheskikh modeley neftyanykh i gazoneftyanykh mestorozhdeniy (Regulation on creation of permanent geological and technological models of oil and gas fields), Moscow: Publ. of Ministry of Fuel and Energy of the Russian Federation, 2000, 129 p.
2. Nasybullin A.V., Antonov O.G., Shutov A.A. et al., 3D reservoir modeling and AI-based optimization of waterflooding system (In Russ.), Neftyanoe
khozyaystvo = Oil Industry, 2012, no. 7, pp. 14–16.
3. Ivanenko B.P., Prokazov S.A., Parfenov A.N., Simulation of the oil production processes using neural networks (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2003, no. 12, pp. 46–49.
4. Rozhkin M.E., Mikliia O.A., Possibilities of modelling work of the well equipment at experimental stands (In Russ.), Avtomatizatsiya, telemekhanizatsiya i svyaz' v neftyanoy promyshlennosti, 2009, no. 8, pp. 16–20.
5. Il'yasov B.G., Tagirova K.F., Dunaev I.V., Automation of oilfield equipment diagnostics using neural networks (In Russ.), Avtomatizatsiya, telemekhanizatsiya i svyaz' v neftyanoy promyshlennosti, 2005, no. 4, pp. 11–18.
6. Tagirova K.F., Avtomatizatsiya upravleniya tekhnologicheskim protsessom
dobychi nefti iz malodebitnykh skvazhin na osnove dinamicheskikh modeley
(Automation of the process control of oil production from low production
wells based on dynamic models): Thesis of candidate of technical science,
Ufa, 2008.
7. Gorban' A.N., Rossiev D.A., Neyronnye seti na personal'nom komp'yutere
(Neural network on the personal computer), Novosibirsk: Nauka Publ., 1996, 276 p. 


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