Bringing the oil wells equipped by elictrical submersible and sucker rod pumps on to stable production based on complex approach using machine learning and digital twins

UDK: 622.276.53.004.14
DOI: 10.24887/0028-2448-2021-7-112-117
Key words: digital twin, bringing well on to stable production, electric submersible pump, sucker rod pump
Authors: A.A. Pashali (Rosneft Oil Company, RF, Moscow), D.V. Silnov (RN-BashNIPIneft LLC, RF, Ufa), A.S. Topolnikov (RN-BashNIPIneft LLC, RF, Ufa), B.M. Latypov (RN-BashNIPIneft LLC, RF, Ufa), R.M. Enikeev (Bashneft PJSOC, RF, Ufa), S.S. Shubin (Bashneft PJSOC, RF, Ufa)

The paper describes the application of modern digital technologies combined within the framework of complex approach during the bringing the oil wells equipped by electrical submersible and sucker rod pumps on to stable production. On the base on the expert rules, digital twins and methods of machine learning the uniform decision support system is developed, which allows to support the process of bringing the well on to stable production beginning from preparation the oil well to startup and finishing when it goes to the normal production. The set of intellectual algorithms is designed, which enable to provide remote diagnostics of complications in the pump operation, lift leaks, operability of measuring systems and to recommend the optimal operating regime, speed of acceleration, configuration of control station, tap transformer, and other activities. On the example of description of scheme of bringing the well, which is equipped by electrical submersible or sucker rod pump, on to stable production the sequence of execution of intellectual algorithms is presented within the framework of complex approach. The results of testing are given, in particular it is shown that the prediction of the well operating regime by digital twin allows to achieve the target parameters and avoid to making the additional bringing on to stable production after the main process is over or, otherwise, to diminish the time of bringing the well to the stable production. With examples of real wells a new method of detecting the direction of rotation of submersible motor shaft is illustrated. The estimation of technical and economic effects of implementation of the complex approach during bringing the well on to stable production is done. The total annual effect for the Bashneft-Dobycha wells achieves 35 million rubles due to diminishing of the number of pump stops and failures during the bringing on to stable production.

References

1. Zhonin V.V., Valiakhmetov R.I., Enikeev R.M. et al., ANK Bashneft on the way to perfection: bringing wells to production as an element of monitoring the mechanical fund (In Russ.), Inzhenernaya praktika, 2015, no. 9, pp. 9–12.

2. Mal'tsev N.V., Prediction of flow characteristics and submersible equipment performance during ESP startup (In Russ.), Neft', gaz i biznes, 2012, no. 8, pp. 72–75.

3. Gribennikov O.A., Mel'nikov A.A., Monitoring of reservoir characteristics by the data of bringing on the well to stable production (In Russ.), Neftepromyslovoe delo, 2020, no. 4(616), pp. 27–31.

4. Pashali A.A., Kolonskikh A.V., Khalfin R.S. et al., A digital twin of well as a tool of digitalization of bringing the well on to stable production in Bashneft PJSOC (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2021, no. 3, pp. 80–85, DOI: 10.24887/0028-2448-2021-3-80-84

5. Volkov M.G., The metodology calculation natural gas separation efficiency during well startup phase (In Russ.), Neftegazovoe delo, 2016, V. 14, no. 4, pp. 45–49.

6. Arkhipov D.S., Latypov B.M., Sil'nov D.V. et al., Ways to improve the energy efficiency of electric submersible pump units for oil production using digital twins (In Russ.), Neftegazovoe delo, 2021, V. 19, no. 1, pp. 42–50.

7. Pashali A.A., Khalfin R.S., Sil'nov D.V. et al., Integrated model “Reservoir – Well – Pump” for unsteady fluid flow regimes calculating (In Russ.), Neftegazovoe delo, 2021, V. 19, no. 1, pp. 33–41, DOI: 10.17122/ngdelo-2021-1-33-418.

8. Volkov M.G., Sil'nov D.V., Topol'nikov A.S. et al., Automated system for interpreting technical condition from dynamograms based on machine learning tools (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2021, no. 4, pp. 102–105, DOI: 10.24887/0028-2448-2021-4-102-105

The paper describes the application of modern digital technologies combined within the framework of complex approach during the bringing the oil wells equipped by electrical submersible and sucker rod pumps on to stable production. On the base on the expert rules, digital twins and methods of machine learning the uniform decision support system is developed, which allows to support the process of bringing the well on to stable production beginning from preparation the oil well to startup and finishing when it goes to the normal production. The set of intellectual algorithms is designed, which enable to provide remote diagnostics of complications in the pump operation, lift leaks, operability of measuring systems and to recommend the optimal operating regime, speed of acceleration, configuration of control station, tap transformer, and other activities. On the example of description of scheme of bringing the well, which is equipped by electrical submersible or sucker rod pump, on to stable production the sequence of execution of intellectual algorithms is presented within the framework of complex approach. The results of testing are given, in particular it is shown that the prediction of the well operating regime by digital twin allows to achieve the target parameters and avoid to making the additional bringing on to stable production after the main process is over or, otherwise, to diminish the time of bringing the well to the stable production. With examples of real wells a new method of detecting the direction of rotation of submersible motor shaft is illustrated. The estimation of technical and economic effects of implementation of the complex approach during bringing the well on to stable production is done. The total annual effect for the Bashneft-Dobycha wells achieves 35 million rubles due to diminishing of the number of pump stops and failures during the bringing on to stable production.

References

1. Zhonin V.V., Valiakhmetov R.I., Enikeev R.M. et al., ANK Bashneft on the way to perfection: bringing wells to production as an element of monitoring the mechanical fund (In Russ.), Inzhenernaya praktika, 2015, no. 9, pp. 9–12.

2. Mal'tsev N.V., Prediction of flow characteristics and submersible equipment performance during ESP startup (In Russ.), Neft', gaz i biznes, 2012, no. 8, pp. 72–75.

3. Gribennikov O.A., Mel'nikov A.A., Monitoring of reservoir characteristics by the data of bringing on the well to stable production (In Russ.), Neftepromyslovoe delo, 2020, no. 4(616), pp. 27–31.

4. Pashali A.A., Kolonskikh A.V., Khalfin R.S. et al., A digital twin of well as a tool of digitalization of bringing the well on to stable production in Bashneft PJSOC (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2021, no. 3, pp. 80–85, DOI: 10.24887/0028-2448-2021-3-80-84

5. Volkov M.G., The metodology calculation natural gas separation efficiency during well startup phase (In Russ.), Neftegazovoe delo, 2016, V. 14, no. 4, pp. 45–49.

6. Arkhipov D.S., Latypov B.M., Sil'nov D.V. et al., Ways to improve the energy efficiency of electric submersible pump units for oil production using digital twins (In Russ.), Neftegazovoe delo, 2021, V. 19, no. 1, pp. 42–50.

7. Pashali A.A., Khalfin R.S., Sil'nov D.V. et al., Integrated model “Reservoir – Well – Pump” for unsteady fluid flow regimes calculating (In Russ.), Neftegazovoe delo, 2021, V. 19, no. 1, pp. 33–41, DOI: 10.17122/ngdelo-2021-1-33-418.

8. Volkov M.G., Sil'nov D.V., Topol'nikov A.S. et al., Automated system for interpreting technical condition from dynamograms based on machine learning tools (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2021, no. 4, pp. 102–105, DOI: 10.24887/0028-2448-2021-4-102-105


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