Skip to main content
SpringerLink
Log in

Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network

  • Conference paper
  • First Online:
Intelligent Systems in Science and Information 2014 (SAI 2014)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 591))

Included in the following conference series:

Abstract

Reservoir simulation provides information about the behaviour of a reservoir in various production and injection conditions. Reservoir simulator is used to predict the future behaviour and performance of a reservoir field. However, the heterogeneity of reservoir and uncertainty in the reservoir field cause some obstacles in selecting the best calculation of oil, water and gas components that lead to the production system in oil and gas. This paper presents a dynamic well Surrogate Reservoir Model (SRM) to predict reservoir bottom-hole flowing pressure by varying the production rate constraint of a well. The proposed SRM adopted Radial Basis Neural Network to predict the bottom-hole flowing pressure of well based on the output data extracted from a numerical simulation model in a considerable amount of time with production constraint values. It is found that the dynamic SRM is capable to generate the promising results in a shorter time as compared to the conventional reservoir model.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pourafshary, P.: A Coupled Wellbore/Reservoir Simulator to Model Multiphase Flow and Temperature Distribution. ProQuest, USA (2007)

    Google Scholar 

  2. Ferro, S.P., Goldschmit, M.B.: A numerical model for multiphase flow on oil production wells. In: Latin American and Caribbean Petroleum Engineering Conference Society of Petroleum Engineers (2007)

    Google Scholar 

  3. Jiang, Y.: Techniques for modeling complex reservoirs and advanced wells. Ph.D. thesis, Stanford University (2007)

    Google Scholar 

  4. Roux, A., Corteville, J., Bernicot, M.: Wellsim and pepite: accurate models of multiphase flow in oil wells and risers (1988)

    Google Scholar 

  5. Mohaghegh, S., Popa, A., Ameri, S.: Intelligent systems can design optimum fracturing jobs (1999)

    Google Scholar 

  6. Queipo, N.V., Salvador, P., Rincon, N., Contreras, N., Colmenares, J.: Surrogate modeling-based optimization for the integration of static and dynamic data into a reservoir description. In: SPE Annual Technical Conference and Exhibition Society of Petroleum Engineers Inc, Dallas. Copyright 2000 (2000)

    Google Scholar 

  7. Queipo, N.V., Goicochea, J.V., Salvador, P.: Surrogate modeling-based optimization of sagd processes (2001)

    Google Scholar 

  8. Mohaghegh, S.D., Hafez, H., Gaskari, R., Haajizadeh, M., Kenawy, M.: Uncertainty analysis of a giant oil field in the middle east using surrogate reservoir model. In: Abu Dhabi International Petroleum Exhibition and Conference Society of Petroleum Engineers, Abu Dhabi (2006)

    Google Scholar 

  9. Mohaghegh, S.D., Modavi, A., Hafez, H., Haajizadeh, M., Kenawy, M., Guruswamy, S.: Development of surrogate reservoir models (SRM) for fast-track analysis of complex reservoirs. In: Intelligent Energy Conference and Exhibition Society of Petroleum Engineers, Amsterdam (2006)

    Google Scholar 

  10. Jalali, J., Mohaghegh, S.D.: Reservoir simulation and uncertainty analysis of enhanced CBM production using artificial neural networks. In: SPE Eastern Regional Meeting. Society of Petroleum Engineers, Charleston (2009)

    Google Scholar 

  11. Hafez, N.A., Haajizadeh, M., Guruswamy, S., Mohaghegh, S.D., Modavi, A.: Development of surrogate reservoir model (SRM) for fast track analysis of a complex reservoir. Int. J. Oil Gas Coal Technol. 2(1), 2–23 (2009)

    Article  Google Scholar 

  12. Mohaghegh, S.D., Jalali, J., Gaskari, R.: Coalbed methane reservoir simulation and uncertainty analysis with artificial neural. Chem. Chem. Eng. 17, 65–76 (2010)

    Google Scholar 

  13. Kalantari Dahaghi, A., Esmaili, S., Mohaghegh, S.D.: Fast track analysis of shale numerical models. In: SPE Canadian Unconventional Resources Conference Society of Petroleum Engineers, Calgary (2012)

    Google Scholar 

  14. Kalantari Dahaghi, A., Mohaghegh, S.D.: Numerical simulation and multiple realizations for sensitivity study of shale gas reservoir. In: SPE Production and Operations Symposium Society of Petroleum Engineers, Oklahoma (2011)

    Google Scholar 

  15. Amini, S., Mohaghegh, S.D., Gaskari, R., Bromhal, G.: Uncertainty analysis of a CO2 sequestration project using surrogate reservoir modeling technique. In: SPE Western Regional Meeting, Society of Petroleum Engineers Bakersfield (2012)

    Google Scholar 

  16. Mohaghegh, S.D., Liu, J.S., Gaskari, R., Maysami, M., Olukoko, O.A.: Application of surrogate reservoir models (SRM) to am onshore green field in saudi arabia; case study. In: North Africa Technical Conference and Exhibition. Society of Petroleum Engineers, Cairo (2012)

    Google Scholar 

  17. Mohaghegh, S.: Virtual-intelligence applications in petroleum engineering: Part 1 artificial neural networks. J. Petrol. Technol. 52(9), 64–73 (2000)

    Article  Google Scholar 

  18. Mohaghegh, S., Arefi, R., Ameri, S., Aminiand, K., Nutter, R.: Petroleum reservoir characterization with the aid of artificial neural networks. J. Petrol. Sci. Eng. 16(4), 263–274 (1996)

    Article  Google Scholar 

  19. El-Sebakhy, E.A., Asparouhov, O., Abdulraheem, A.A., Al-Majed, A.A., Wu, D., Latinski, K., Raharja, I.: Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir. Expert Syst. Appl. 39(12), 10359–10375 (2012)

    Article  Google Scholar 

  20. Tahmasebi, P., Hezarkhani, A.: A fast and independent architecture of artificial neural network for permeability prediction. J. Petrol. Sci. Eng. 86, 118–126 (2012)

    Article  Google Scholar 

  21. Mohammadpoor, M., Shahbazi, K., Torabi, F., Reza, A., Firouz, Q.: A new methodology for prediction of bottomhole flowing pressure in vertical multiphase flow in iranian oil fields using artificial neural networks (anns) (2010)

    Google Scholar 

  22. Osman, E.S.A., Ayoub, M.A., Aggour, M.A.: Artificial neural network model for predicting bottomhole flowing pressure in vertical multiphase flow. In: SPE Middle East Oil and Gas Show and Conference Society of Petroleum Engineers, Kingdom of Bahrain (2005)

    Google Scholar 

  23. PAlrumah, M., Startzman, R., Schechter, D., Ibrahim, M.: Predicting well inflow performance in solution gas drive reservoir by neural network (2005)

    Google Scholar 

  24. Ramgulam, A., Ertekin, T., Flemings, P.B.: An artificial neural network utility for the optimization of history matching process. In: Latin American and Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers (2007)

    Google Scholar 

  25. Carnevale, C., Finzi, G., Guariso, G., Pisoni, E., Volta, M.: Surrogate models to compute optimal air quality planning policies at a regional scale. Environ. Model Softw. 34, 44–50 (2012)

    Article  Google Scholar 

  26. Arastoopour, H., Hariri, H.: Analysis of two-phase flow in a tight sand gas reservoir, using boast (1986)

    Google Scholar 

  27. Bujnowski, S.W., Fanchi, J.R., Harpole, K.J.: Boast: a three-dimensional, three-phase black oil applied simulation tool (version 1.1). Bartlesville Project Office U.S. department of energy Bartlesville, Oklahoma (1982)

    Google Scholar 

  28. Sampaio, T.P., Ferreira Filho, V.J.M., Neto, A.D.S.: An application of feed forward neural network as nonlinear proxies for use during the history matching phase. In: Latin American and Caribbean Petroleum Engineering Conference Society of Petroleum Engineers, Cartagena de Indias (2009)

    Google Scholar 

  29. Vogl, T.P., Mangis, J.K., Rigler, A.K., Zink, W.T., Alkon, D.L.: Accelerating the convergence of the back-propagation method. Biol. Cybern. 59(4–5), 257–263 (1988)

    Article  Google Scholar 

  30. Musavi, M.T., Ahmed, W., Chan, K.H., Faris, K.B., Hummels, D.M.: On the training of radial basis function classifiers. Neural Networks 5(4), 595–603 (1992)

    Article  Google Scholar 

  31. Williams, M.: Application of artificial neural networks in the quantitative analysis of gas chromatograms. Master’s thesis (1996)

    Google Scholar 

  32. Yingwei, L., Sundararajan, N., Saratchandran, P.: Radial basis function neural networks with sequential learning. World Scientific Publishing Co.. Inc., Singapore (1999)

    Google Scholar 

  33. Kaftan, I., Salk, M.: Determination of structure parameters on gravity method by using radial basis functions networks case study: Seferihisar geothermal area (western turkey). In 2009 SEG Annual Meeting. Society of Exploration Geophysicists (2009)

    Google Scholar 

  34. Huang, K.Y., Shen, L.C., Weng L.S.: Well log data inversion using radial basis function network. In Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, pp 4439–4442. IEEE (2011)

    Google Scholar 

  35. Russell, B.H., Hampson, D.P., Lines, L.R.: Application of the radial basis function neural network to the prediction of log properties from seismic attributes—a channel sand case study (2003)

    Google Scholar 

  36. Li, L., Wei, X., Shifan, Z., Wan, Z.: Reservoir property prediction using the dynamic radial basis function network (2011)

    Google Scholar 

  37. Gengaje, S.R., Alandkar, L.S.: Prediction of survival of burn patient using radial basis function network (1988)

    Google Scholar 

  38. Edara, P.K.: Mode choice modeling using artificial neural network. Master’s thesis, Virginia Polytechnic Institute and State University (2003)

    Google Scholar 

  39. Odeh, A.S.: Comparison of solutions to a three-dimensional black-oil reservoir simulation problem. J. Petrol. Technology 33, 13–25 (1981)

    Article  Google Scholar 

  40. Are, S., Dostert, P., Texas, A.M., Ettinger, B., Liu, J., Sokolov, V., Wei, A.: Reservoir model optimization under uncertainty (2006)

    Google Scholar 

Download references

Acknowledgment

The authors also like to thank Universiti Teknologi PETRONAS for sponsoring the project funding under YUTP-EOR MOR.

Author information

Authors and Affiliations

  1. Computer Information and Sciences, Universiti Teknologi Petronas, 31750, Tronoh, Malaysia

    Paras Q. Memon & Suet-Peng Yong

  2. Department of Mechanical Engineering, Universiti Teknologi Petronas, 31750, Tronoh, Malaysia

    William Pao & Jion Sean Pau

Authors
  1. Paras Q. Memon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suet-Peng Yong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William Pao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jion Sean Pau
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suet-Peng Yong .

Editor information

Editors and Affiliations

  1. Graduate School of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, London, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, London, United Kingdom

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Memon, P.Q., Yong, SP., Pao, W., Pau, J.S. (2015). Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems in Science and Information 2014. SAI 2014. Studies in Computational Intelligence, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-14654-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14654-6_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14653-9

  • Online ISBN: 978-3-319-14654-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation