Skip to main content

Advertisement

SpringerLink
Log in

Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning

  • Original Paper
  • Published:
Computational Geosciences Aims and scope Submit manuscript

Abstract

Water coning is one of the common issues in subsurface systems in which water flows into the production well through perforated zones. This phenomenon can cause severe problems in wellbore and surface facilities. Thus, accurate prediction of water breakthrough can help to adapt to the production mode and avoid such issues. Conducting flow simulations, as a conventional approach, can be very time demanding if one deals with large subsurface systems. Furthermore, several types of data are often collected during the life of a subsurface system each of which can help to predict the breakthrough and water coning. As such, it is very important to produce similar results using the time sequence data gathered from various geo-sensing tools. In this paper, a deep long short-term memory (LSTM) model is developed to predict the water cut and water breakthrough time for multiple production wells in a water flooding case. The dataset is generated by the Egg model with a multi-input-multi-output system. We found that the proposed model can capture the general trend of variation for the water cut time sequence data for a complex subsurface system. To evaluate the performance of our data-driven method, the results are compared with vanilla recurrent neural network (RNN), deep gated recurrent unit (GRU), and artificial neural network (ANN). The conducted comparison indicates that the proposed deep LSTM model outperforms the other three approaches when the results are compared with the numerical data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmed, T.H.: Reservoir engineering handbook. Gulf Pub. Co. (2000)

  2. Hoyland, L.A., Papatzacos, P., Skjaeveland, S.M.: Critical rate for water coning. Correlation and analytical solution. SPE Reserv. Eng. Society Pet. Eng. 4, 495–502 15855 (1989). https://doi.org/10.2118/15855-pa

    Article  Google Scholar 

  3. Wagenhofer, T., Hatzignatiou, D.G.: Optimization of horizontal well placement. In: SPE Western Regional Meeting. Society of Petroleum Engineers (1996)

    Google Scholar 

  4. Muskat, M., Wycokoff, R.D.: An approximate theory of water-coning in oil production. Trans. AIME. 114, 144–163 (1935). https://doi.org/10.2118/935144-g

    Article  Google Scholar 

  5. Chaney, P.E., Noble, M.D., Henson, W.L., Rice, T.D.: How to perforate your well to prevent water and gas coning. Oil Gas J. 55, 108–114 (1956)

    Google Scholar 

  6. Chierici, G.L., Ciucci, G.M., Pizzi, G.: A systematic study of gas and water coning by potentiometric models. J. Pet. Technol. 16, 923–929 (1964). https://doi.org/10.2118/871-pa

    Article  Google Scholar 

  7. Wheatley, M.J.: An approximate theory of oil/water coning. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers (1985)

    Google Scholar 

  8. Abass, H.H., Bass, D.M.: The critical production rate in water-coning system. In: Permian Basin Oil and Gas Recovery Conference. Society of Petroleum Engineers (1988)

    Google Scholar 

  9. Guo, B., Lee, R.L.H.: Simple approach to optimization of completion interval in oil/water coning systems. SPE Reserv. Eng. Society Pet. Eng. 8, 249–255 (1993). https://doi.org/10.2118/23994-pa

    Article  Google Scholar 

  10. Schols, R.S.: An empirical formula for the critical oil production rate. Erdoel Erdgas. 88, 6–11 (1972)

    Google Scholar 

  11. Zendehboudi, S., Elkamel, A., Chatzis, I., Ahmadi, M.A., Bahadori, A., Lohi, A.: Estimation of breakthrough time for water coning in fractured systems: experimental study and connectionist modeling. AICHE J. 60, 1905–1919 (2014). https://doi.org/10.1002/aic.14365

    Article  Google Scholar 

  12. Ozkan, E., Raghavan, R.: Performance of horizontal wells subject to bottomwater drive. SPE Reserv. Eng. Society Pet. Eng. 5, 375–383 (1990). https://doi.org/10.2118/18559-PA

    Article  Google Scholar 

  13. Yang, W., Wattenbarger, R.A.: Water coning calculations for vertical and horizontal wells. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers (1991)

    Google Scholar 

  14. Papatzacos, P., Herring, T.R., Martinsen, R., Skjaeveland, S.M.: Cone breakthrough time for horizontal wells. SPE Reserv. Eng. Society Pet. Eng. 6, 311–318 (1991). https://doi.org/10.2118/19822-PA

    Article  Google Scholar 

  15. Omeke, J.E., Livinus, A., Uche, I.N., Obah, B., Ekeoma, E.: A proposed cone breakthrough time model for horizontal wells in thin oil rim reservoirs. In: Society of Petroleum Engineers - Nigeria Annual International Conference and Exhibition 2010, NAICE, pp. 963–973 (2010)

    Google Scholar 

  16. Ahmadi, M.A., Ebadi, M., Hosseini, S.M.: Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach. Fuel. 117, 579–589 (2014). https://doi.org/10.1016/j.fuel.2013.09.071

    Article  Google Scholar 

  17. Bai, T., Tahmasebi, P.: Hybrid geological modeling: combining machine learning and multiple-point statistics. Comput. Geosci. 104519 (2020). https://doi.org/10.1016/j.cageo.2020.104519

  18. Kamrava, S., Tahmasebi, P., Sahimi, M.: Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm. Neural Netw. 118, 310–320 (2019). https://doi.org/10.1016/J.NEUNET.2019.07.009

    Article  Google Scholar 

  19. Kamrava, S., Tahmasebi, P., Sahimi, M.: Linking morphology of porous media to their macroscopic permeability by deep learning. Transp. Porous Media. 131, 427–448 (2019). https://doi.org/10.1007/s11242-019-01352-5

    Article  Google Scholar 

  20. Kamrava, S., Sahimi, M., Tahmasebi, P.: Quantifying accuracy of stochastic methods of reconstructing complex materials by deep learning. Phys. Rev. E. 101, 043301 (2020). https://doi.org/10.1103/PhysRevE.101.043301

    Article  Google Scholar 

  21. Tahmasebi, P., Sahimi, M., Shirangi, M.G.: Rapid learning-based and geologically consistent history matching. Transp. Porous Media. 122, 279–304 (2018). https://doi.org/10.1007/s11242-018-1005-6

    Article  Google Scholar 

  22. Tahmasebi, P., Kamrava, S., Bai, T., Sahimi, M.: Machine learning in geo- and environmental sciences: from small to large scale. Adv. Water Resour. 142, 103619 (2020). https://doi.org/10.1016/j.advwatres.2020.103619

    Article  Google Scholar 

  23. Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE. 78, 1550–1560 (1990). https://doi.org/10.1109/5.58337

    Article  Google Scholar 

  24. Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5, 157–166 (1994). https://doi.org/10.1109/72.279181

    Article  Google Scholar 

  25. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  26. Nelson, D.M.Q., Pereira, A.C.M., De Oliveira, R.A.: Stock market’s price movement prediction with LSTM neural networks. In: Proceedings of the International Joint Conference on Neural Networks. pp. 1419–1426. Institute of Electrical and Electronics Engineers Inc. (2017)

  27. Zhang, Q., Wang, H., Dong, J., Zhong, G., Sun, X.: Prediction of sea surface temperature using long short-term memory. IEEE Geosci. Remote Sens. Lett. 14, 1745–1749 (2017). https://doi.org/10.1109/LGRS.2017.2733548

    Article  Google Scholar 

  28. Zhao, J., Deng, F., Cai, Y., Chen, J.: Long short-term memory - fully connected (LSTM-FC) neural network for PM2.5 concentration prediction. Chemosphere. 220, 486–492 (2019). https://doi.org/10.1016/j.chemosphere.2018.12.128

    Article  Google Scholar 

  29. Du, B., Peng, H., Wang, S., Bhuiyan, M.Z.A., Wang, L., Gong, Q., Liu, L., Li, J.: Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Trans. Intell. Transp. Syst, pp. 1–14 (2019). https://doi.org/10.1109/tits.2019.2900481

    Book  Google Scholar 

  30. Kamrava, S., Tahmasebi, P., Sahimi, M., Arbabi, S.: Phase transitions, percolation, fracture of materials, and deep learning. Phys. Rev. E. 102, 011001 (2020). https://doi.org/10.1103/PhysRevE.102.011001

    Article  Google Scholar 

  31. Jansen, J.D., Fonseca, R.M., Kahrobaei, S., Siraj, M.M., Van Essen, G.M., Van den Hof, P.M.J.: The egg model - a geological ensemble for reservoir simulation. Geosci. Data J. 1, 192–195 (2014). https://doi.org/10.1002/gdj3.21

    Article  Google Scholar 

  32. Nasrabadi, N.M., Choo, C.Y.: Hopfield network for stereo vision correspondence. IEEE Trans. Neural Netw. 3, 5–13 (1992). https://doi.org/10.1109/72.105413

    Article  Google Scholar 

  33. Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. (2012)

    Google Scholar 

  34. Gers, F.: Long short-term memory in recurrent neural networks, (2001)

    Google Scholar 

  35. Tahmasebi, P., Kamrava, S.: A multiscale approach for geologically and flow consistent modeling. Transp. Porous Media. 124, 237–261 (2018). https://doi.org/10.1007/s11242-018-1062-x

    Article  Google Scholar 

  36. Tahmasebi, P.: Multiple point statistics: a review. In: Handbook of Mathematical Geosciences, pp. 613–643. Springer International publishing, Cham (2018)

    Chapter  Google Scholar 

  37. Alumbaugh, D.L., Morrison, H.F.: Theoretical and practical considerations for crosswell electromagnetic tomography assuming a cylindrical geometry. Geophysics. 60, 846–870 (1995). https://doi.org/10.1190/1.1443822

    Article  Google Scholar 

  38. Wilt, M.J., Alumbaugh, D.L., Morrison, H.F., Becker, A., Lee, K.H., Deszcz-Pan, M.: Crosswell electromagnetic tomography: system design considerations and field results. Geophysics. 60, 871–885 (1995). https://doi.org/10.1190/1.1443823

    Article  Google Scholar 

  39. Constable, S., Cox, C.S.: Marine controlled-source electromagnetic sounding: 2 the PEGASUS experiment. J. Geophys. Res. Solid Earth. 101, 5519–5530 (1996). https://doi.org/10.1029/95jb03738

    Article  Google Scholar 

  40. MacGregor, L., Sinha, M., Constable, S.: Electrical resistivity structure of the Valu Fa Ridge, Lau Basin, from marine controlled-source electromagnetic sounding. Geophys. J. Int. 146, 217–236 (2001). https://doi.org/10.1046/j.1365-246X.2001.00440.x

    Article  Google Scholar 

  41. Archie, G.E.: The electrical resistivity log as an aid in determining some reservoir characteristics. Pet. Trans. AIME. 146, 54–62 (1942). https://doi.org/10.2118/942054-G

    Article  Google Scholar 

  42. Raghu, M., Poole, B., Kleinberg, J., Ganguli, S., Dickstein, J.S.: On the expressive power of deep neural networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. pp. 2847–2854. JMLR.org (2017)

  43. Eldan, R., Shamir, O.: The power of depth for feedforward neural networks. In: Journal of Machine Learning Research. pp. 907–940. Microtome Publishing (2016)

  44. Hermans, M., Schrauwen, B.: Training and analysing deep recurrent neural networks. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., and Weinberger, K.Q. (eds.) Advances in neural information processing systems 26. pp. 190–198. Curran Associates, Inc. (2013)

  45. Langkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn. Lett. 42, 11–24 (2014). https://doi.org/10.1016/j.patrec.2014.01.008

    Article  Google Scholar 

  46. Spiegel, S., Gaebler, J., Lommatzsch, A., De Luca, E., Albayrak, S.: Pattern recognition and classification for multivariate time series. In: Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, pp. 34–42. Association for Computing Machinery, New York (2011)

    Chapter  Google Scholar 

  47. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. (2014)

    Google Scholar 

  48. Chollet, F., others: Keras (2015), (2017)

  49. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    Google Scholar 

Download references

Acknowledgments

The comments from two anonymous referees are also acknowledged.

Funding

This work was financially supported by the School of Energy Resources and the University of Wyoming.

Author information

Authors and Affiliations

  1. Department of Petroleum Engineering, University of Wyoming, Laramie, WY, 82071, USA

    Tao Bai & Pejman Tahmasebi

Authors
  1. Tao Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pejman Tahmasebi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pejman Tahmasebi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bai, T., Tahmasebi, P. Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning. Comput Geosci 25, 285–297 (2021). https://doi.org/10.1007/s10596-020-10005-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-020-10005-2

Keywords

Search

Navigation