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Artificial intelligence hybrid methods application in the task of the heavy oilfields profitability increase

Authors: I.S. Korovin, M.G. Tkachenko, M.V. Khisamutdinov, A.I.Kalyaev (Scientific research institute of multiprocessor computing systems, RF, Taganrog)

Key words: heavy oil, primary cost reduction, data mining, artificial neural networks, genetic algorithms, methods of enhanced oil recovery (EOR), retrospective analysis

The article considers the problem of heavy oil production increasing by EOR methods. A new approach, based on the intelligent analysis of successful events historical data, is proposed. Based on database info analysis, the synthesis of a model, targeted to automated search of wells for EOR application, is worked out. The main data processing tool is a novel technology, based on neural network analysis techniques and evolutionary algorithms implementation. This approach allows to select EOR in fuzzy, hardly formalized oilfield conditions and reduces the dependence on the human factor.

References
1. URL: http://www.vedomosti.ru/business/articles/2011/05/25/neft_potyazhelela.
2. URL: http://www.cdu.ru/articles/detail.php?ID=301905.
3. URL: http://one_vision.jofo.ru/241887.html.
4. URL: http://refdb.ru/look/3084773.html.
5. URL: http://to-inform.ru/index.php/arkhiv/item/dobycha-vysokovyazkoynefti.
6. URL: http://www.ng.ru/energy/2013-04-09/14_alternative.html.
7. Korovin Ya.S., Decision support system for electrical submersible pumps
control on the neural network basis (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2007, no. 1, pp. 80-83.
8. Korovin Ya.S., Tkachenko M.G., Implementation of neural network data
analysis in oil and gas extraction industry (In Russ.), Izvestiya Yuzhnogo federal'nogo universiteta. Tekhnicheskie nauki, 2010, no. 12, pp. 172-178.
9. Korovin Ya.S., Kononov S.V., Tkachenko M.G., Oilfield equipment's state diagnostics on the basis of data mining technologies (In Russ.), Neftyanoe
khozyaystvo = Oil Industry, 2012, no. 9, pp. 116-118.
10. Korovin Ya.S., Khisamutdinov M.V., Tkachenko M.G., Forecasting of oilfield equipment work conditions with the application of evolutionary algorithms and artificial neural networks (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2013, no. 12, pp. 128-133.

Key words: heavy oil, primary cost reduction, data mining, artificial neural networks, genetic algorithms, methods of enhanced oil recovery (EOR), retrospective analysis

The article considers the problem of heavy oil production increasing by EOR methods. A new approach, based on the intelligent analysis of successful events historical data, is proposed. Based on database info analysis, the synthesis of a model, targeted to automated search of wells for EOR application, is worked out. The main data processing tool is a novel technology, based on neural network analysis techniques and evolutionary algorithms implementation. This approach allows to select EOR in fuzzy, hardly formalized oilfield conditions and reduces the dependence on the human factor.

References
1. URL: http://www.vedomosti.ru/business/articles/2011/05/25/neft_potyazhelela.
2. URL: http://www.cdu.ru/articles/detail.php?ID=301905.
3. URL: http://one_vision.jofo.ru/241887.html.
4. URL: http://refdb.ru/look/3084773.html.
5. URL: http://to-inform.ru/index.php/arkhiv/item/dobycha-vysokovyazkoynefti.
6. URL: http://www.ng.ru/energy/2013-04-09/14_alternative.html.
7. Korovin Ya.S., Decision support system for electrical submersible pumps
control on the neural network basis (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2007, no. 1, pp. 80-83.
8. Korovin Ya.S., Tkachenko M.G., Implementation of neural network data
analysis in oil and gas extraction industry (In Russ.), Izvestiya Yuzhnogo federal'nogo universiteta. Tekhnicheskie nauki, 2010, no. 12, pp. 172-178.
9. Korovin Ya.S., Kononov S.V., Tkachenko M.G., Oilfield equipment's state diagnostics on the basis of data mining technologies (In Russ.), Neftyanoe
khozyaystvo = Oil Industry, 2012, no. 9, pp. 116-118.
10. Korovin Ya.S., Khisamutdinov M.V., Tkachenko M.G., Forecasting of oilfield equipment work conditions with the application of evolutionary algorithms and artificial neural networks (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2013, no. 12, pp. 128-133.


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