Automated intelligent assistant in the selection of well placement when developing hard-to-recover reserves

UDK: 681.518:622.276.1/.4
DOI: 10.24887/0028-2448-2020-10-76-81
Key words: hard-to-recover reserves, digitalization, increase in efficiency
Authors: A.V. Sergeychev (Rosneft Oil Company, RF, Moscow), K.V. Toropov (Rosneft Oil Company, RF, Moscow), M.S. Antonov (RN-BashNIPIneft LLC, RF, Ufa), A.E. Fedorov (RN-BashNIPIneft LLC, RF, Ufa), A.A. Povalyaev (RN-BashNIPIneft LLC, RF, Ufa), I.R. Dilmuhametov (RN-BashNIPIneft LLC, RF, Ufa), O.V. Nadezdin (RN-BashNIPIneft LLC, RF, Ufa)

Nowadays, due to continuous depletion of conventional reserves, oil producers seek alternatives for resource base maintenance in unconventional hard-to-recover deposits. In particular, Rosneft Oil Company every year increase the number of new wells drilled in low-permeable ((0.1-0.5) 10-3 mkm2) Achimov formation reservoirs with high vertical heterogeneity, weak connectivity between sand bodies and net-to-gross ratio between 10 and 30%.Currently, a large number of studies are being carried out in the design of optimal systems for the development of low-permeability and low-connectivity reservoirs and methods for creating physically meaningful models for a technical and economic assessment of the effectiveness of design solutions. However, few of them describe systematic approaches to the selection of optimal development systems and, in particular, to making decisions about adjusting the development mode.

The objective of this work is to provide a description of the Automated Intelligent Assistant, “Decision Support System for Tight Oil Fields Development”, that enables an automatic selection of the optimal wells placement patterns for prospective drilling areas in unconventional reservoirs. The article describes the main parts of the system integrated into a complex module Smart-GIR in Rosneft Oil Company corporate software package RN-KIN. The proposed solution engages machine learning algorithms in its workflow. The project’s scope includes the development of the algorithms for reservoirs cauterization in Achimov deposits and their analogs and creation the database that comprises the interpreted output of multivariate reservoir simulation and developed a neural network for replication of numerical calculations.

References

1. Baykov V.A., Galeev R.R., Kolonskikh A.V. et al., Nonlinear filtration in low-permeability reservoirs. Impact on the technological parameters of the field development (In Russ.), Nauchno-tekhnicheskiy vestnik OAO “NK “Rosneft'”, 2013, no. 2, pp.  17–19.

2. Belonogov E.V., Pustovskikh A.A., Samolovov D.A., Methodology for determination of low-permeability reservoirs optimal development plan (In Russ.),

SPE-182041-RU, 2016.

3. Galeev R.R., Zorin A.M., Kolonskikh A.V. et al., Optimal waterflood pattern selection with use of multiple fractured horizontal wells for development of the low-permeability formations (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2013, no. 10, pp. 62–65.

4. Zakrevskiy K.E., Popov V.L., Variogram analysis of geological bodies (In Russ.), Ekspozitsiya Neft' Gaz, 2018, no. 1, pp. 27–31.

5. Zakrevskiy K.E., Lepilin A.E., Novikov A.P., The parameter interdependency analysis for geological hydrocarbon field modeling (In Russ.), Territoriya Neftegaz, 2018, no. 10, pp. 20–26.

6. Krasnov V.A., Sudeev I.V., Yudin E.V. et al., Reservoir parameters evaluation using the production data analysis (In Russ.), Nauchno-tekhnicheskiy Vestnik OAO “NK “Rosneft'”, 2010, no. 1, pp. 30–34.

7. Nurlyev D.R., Rodionova I.I., Viktorov E.P. Et al., Tight reservoir simulation study under geological and technological uncertainty (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2018, no. 10, pp. 60–63.

8. Rodionova I.I., Shabalin M.A., Mironenko A.A., Khabibullin G.I., Field development plan and well completion system optimization for ultra-tight and ultra-heterogeneous oil reservoirs (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2019, no. 10, pp. 72–76.

9. Timonov A.V., Sergeychev A.V., Yamalov I.R. et al., Influence of reservoir heterogeneity characteristics on ultimate oil recovery in Priobskoye field (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2012, no. 11, pp. 38–40.

10. Fedorov A.E., Amineva A.A., Dil'mukhametov I.R. et al., Analysis of geological heterogeneity in geological stochastic modeling (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2019, no. 9, pp. 24–28.

11. Fedorov A.E., Dil'mukhametov I.R., Povalyaev A.A. et al., Multivariate optimization of the system for the development of low-permeability reservoirs of oil fields of the Achimov formation (In Russ.), SPE-201811-RU, 2020.

12. Fedorov A.E., Suleymanov B.I., Povalyaev A.A. et al., Decision support system for drilling new sections of low-permeability reservoirs of the Achimov deposits and their analogues using machine learning algorithms (In Russ.),

SPE-201921-RU, 2020.

13. Larue  D.K., Hovadik J., Connectivity of channelized reservoirs: a modelling approach, Petroleum Geoscience, 2006, V. 12, pp. 291–308.

14. Povalyaev A.A., Fedorov A.E., Suleymanov B.I. et al., Application of artificial intelligence algorithms for tight oil field development, Proceeding of First EAGE Digitalization Conference and Exhibition, 2020, pp. 1–5.

15. Shabalin M., Khabibullin G., Suleymanov E. et al., Tight oil development

in RN-Yuganskneftegas (In Russ.), SPE-196753-MS, 2019.

Nowadays, due to continuous depletion of conventional reserves, oil producers seek alternatives for resource base maintenance in unconventional hard-to-recover deposits. In particular, Rosneft Oil Company every year increase the number of new wells drilled in low-permeable ((0.1-0.5) 10-3 mkm2) Achimov formation reservoirs with high vertical heterogeneity, weak connectivity between sand bodies and net-to-gross ratio between 10 and 30%.Currently, a large number of studies are being carried out in the design of optimal systems for the development of low-permeability and low-connectivity reservoirs and methods for creating physically meaningful models for a technical and economic assessment of the effectiveness of design solutions. However, few of them describe systematic approaches to the selection of optimal development systems and, in particular, to making decisions about adjusting the development mode.

The objective of this work is to provide a description of the Automated Intelligent Assistant, “Decision Support System for Tight Oil Fields Development”, that enables an automatic selection of the optimal wells placement patterns for prospective drilling areas in unconventional reservoirs. The article describes the main parts of the system integrated into a complex module Smart-GIR in Rosneft Oil Company corporate software package RN-KIN. The proposed solution engages machine learning algorithms in its workflow. The project’s scope includes the development of the algorithms for reservoirs cauterization in Achimov deposits and their analogs and creation the database that comprises the interpreted output of multivariate reservoir simulation and developed a neural network for replication of numerical calculations.

References

1. Baykov V.A., Galeev R.R., Kolonskikh A.V. et al., Nonlinear filtration in low-permeability reservoirs. Impact on the technological parameters of the field development (In Russ.), Nauchno-tekhnicheskiy vestnik OAO “NK “Rosneft'”, 2013, no. 2, pp.  17–19.

2. Belonogov E.V., Pustovskikh A.A., Samolovov D.A., Methodology for determination of low-permeability reservoirs optimal development plan (In Russ.),

SPE-182041-RU, 2016.

3. Galeev R.R., Zorin A.M., Kolonskikh A.V. et al., Optimal waterflood pattern selection with use of multiple fractured horizontal wells for development of the low-permeability formations (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2013, no. 10, pp. 62–65.

4. Zakrevskiy K.E., Popov V.L., Variogram analysis of geological bodies (In Russ.), Ekspozitsiya Neft' Gaz, 2018, no. 1, pp. 27–31.

5. Zakrevskiy K.E., Lepilin A.E., Novikov A.P., The parameter interdependency analysis for geological hydrocarbon field modeling (In Russ.), Territoriya Neftegaz, 2018, no. 10, pp. 20–26.

6. Krasnov V.A., Sudeev I.V., Yudin E.V. et al., Reservoir parameters evaluation using the production data analysis (In Russ.), Nauchno-tekhnicheskiy Vestnik OAO “NK “Rosneft'”, 2010, no. 1, pp. 30–34.

7. Nurlyev D.R., Rodionova I.I., Viktorov E.P. Et al., Tight reservoir simulation study under geological and technological uncertainty (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2018, no. 10, pp. 60–63.

8. Rodionova I.I., Shabalin M.A., Mironenko A.A., Khabibullin G.I., Field development plan and well completion system optimization for ultra-tight and ultra-heterogeneous oil reservoirs (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2019, no. 10, pp. 72–76.

9. Timonov A.V., Sergeychev A.V., Yamalov I.R. et al., Influence of reservoir heterogeneity characteristics on ultimate oil recovery in Priobskoye field (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2012, no. 11, pp. 38–40.

10. Fedorov A.E., Amineva A.A., Dil'mukhametov I.R. et al., Analysis of geological heterogeneity in geological stochastic modeling (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2019, no. 9, pp. 24–28.

11. Fedorov A.E., Dil'mukhametov I.R., Povalyaev A.A. et al., Multivariate optimization of the system for the development of low-permeability reservoirs of oil fields of the Achimov formation (In Russ.), SPE-201811-RU, 2020.

12. Fedorov A.E., Suleymanov B.I., Povalyaev A.A. et al., Decision support system for drilling new sections of low-permeability reservoirs of the Achimov deposits and their analogues using machine learning algorithms (In Russ.),

SPE-201921-RU, 2020.

13. Larue  D.K., Hovadik J., Connectivity of channelized reservoirs: a modelling approach, Petroleum Geoscience, 2006, V. 12, pp. 291–308.

14. Povalyaev A.A., Fedorov A.E., Suleymanov B.I. et al., Application of artificial intelligence algorithms for tight oil field development, Proceeding of First EAGE Digitalization Conference and Exhibition, 2020, pp. 1–5.

15. Shabalin M., Khabibullin G., Suleymanov E. et al., Tight oil development

in RN-Yuganskneftegas (In Russ.), SPE-196753-MS, 2019.



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