Research Article
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Year 2018, Volume: 2 Issue: 2, 124 - 131, 15.08.2018

Abstract

References

  • 1. Bourgoyne, A.T., M.E. Chenevert, K.K. Millheim, and F.S. Young, 1991, Applied drilling engineering. SPE Textbook Series, vol. 2. Richardson, TX.
  • 2. Rooki, R., Ardejani, F. D., Moradzadeh, A., Mirzaei, H., Kelessidis, V., Maglione, R., and M. Norouzi, Optimal determination of rheological parameters for herschel-bulkley drilling fluids using genetic algorithms (GAs). Korea-Australia Rheology Journal, 2012. 24(3): p. 163-170.
  • 3. Elkatatny, S., Real-Time Prediction of Rheological Parameters of KCl Water-Based Drilling Fluid Using Artificial Neural Networks. Arabian Journal for Science and Engineering, 2017. 42(4): p. 1655-1665.
  • 4. Ali, J.K., Neural networks: a new tool for the petroleum industry., Paper SPE 27561 Presented at the 1994 European Computer Conference, Aberdeen, p. 15 – 17.
  • 5. González, A., Barrufet, M. A., and R. Startzman, Improved neural-network model predicts dewpoint pressure of retrograde gases. Journal of Petroleum Science and Engineering, 2003. 37(3): p. 183-194.
  • 6. Mohaghegh, S., Neural network: what it can do for petroleum engineers. Journal of Petroleum Technology, 1995. 47(01): p. 42-42.
  • 7. Mohaghegh, S., Virtual-intelligence applications in petroleum engineering: Part 1—Artificial neural networks. Journal of Petroleum Technology, 2000. 52(09): p. 64-73.
  • 8. Elkatatny, S., Tariq, Z., and M. Mahmoud, Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box). Journal of Petroleum Science and Engineering, 2016. 146: p. 1202-1210.
  • 9. da Silva Bispo, V. D., Scheid, C. M., Calçada, L. A., and L. A. da Cruz Meleiro, Development of an ANN-based soft-sensor to estimate the apparent viscosity of water-based drilling fluids. Journal of Petroleum Science and Engineering, 2017. 150: p. 69-73.
  • 10. Fann Instrument Company, Available from: http://www.fann.com/public1/pubsdata/Brochures/Mixers2.pdf
  • 11. Fann Instrument Company, Available from: http://www.fann.com/public1/pubsdata/Brochures/Rheometer_Model_50.pdf
  • 12. Ağyar, Z., Yapay sinir ağlarının kullanım alanları ve bir uygulama, Mühendis ve Makine Dergisi, 2015. 56(662): p. 22-23.
  • 13. Ataseven, B., Yapay sinir ağları ile öngörü modellemesi, 2013.Öneri Dergisi, 10(39), p. 101-115.
  • 14. Rooki, R., Estimation of pressure loss of Herschel–Bulkley drilling fluids during horizontal annulus using artificial neural network. Journal of Dispersion Science and Technology, 2015. 36(2): p. 161-169.
  • 15. Haykin, S., 1999, Neural Networks: A Comprehensive Foundation, 2nd ed.; Upper Saddle River, NJ: Prentice Hall
  • 16. Beale, M. H., M. T. Hagan,. and H. B. Demuth, Neural Network Toolbox™ User's Guide. The Mathworks Inc, 2017.
  • 17. Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., Van Dongen, B.F., Kindler, E. and C.W. Günther, Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 2010. 9(1): p. 87–111.
  • 18. Hussain, Q. E., and Sharif, M. A. R. Numerical modeling of helical flow of viscoplastic fluids in eccentric annuli, 2000. AIChE journal, 46(10), 1937-1946.
  • 19. Specht, L. P., Khatchatourian, O., Brito, L. A. T., and Ceratti, J. A. P. Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks. Materials Research, 2007. 10(1), 69-74.
  • 20. Elkatatny, S., Tariq, Z., and Mahmoud, M. Real time prediction of drilling fluid rheological properties using artificial neural networks visible mathematical model (white box). 2016. Journal of Petroleum Science and Engineering, 146, 1202-1210.
  • 21. Avcı, E., Effects of the geothermal water based muds on the drilling performance, Master Thesis, İskenderun Technical University, Institute of Science, 2018 (in Turkish, unpublished).

An Artificial Neural Network Approach for the prediction of Water-Based Drilling Fluid Rheological Behaviour

Year 2018, Volume: 2 Issue: 2, 124 - 131, 15.08.2018

Abstract

It is well known that high temperatures, which change
the rheological properties of the drilling fluid and can frequently cause
problems in deep wells, is a major problem during drilling. The importance of
the estimation and control of the rheological parameters of the drilling fluid
and the hydraulics of the well  increases
as the depth of the well drilled is being increased to explore new oil, gas or
geothermal reserves.
Since it is difficult to measure these parameters with
standard field and laboratory viscometers, different conventional measurements
and regression-analysis techniques are routinely used to approximate the true
rheological parameters. In this study,  water-based
drilling fluid was initially prepared and rheological properties of the fluids
were measured under elevated temperatures using high temperature rheometer (Fann
Model 50 SL). Then, the shear stresses of drilling fluid are predicted using
artificial neural network (ANN) method depending on the elevated temperature
and shear rate. The results obtained from the high temperature rheometer and
artificial neural network were compared with each other and analyzed.
Consequently, it is observed that the artificial neural network could be used
with good engineering accuracy to directly estimate the shear stress of
drilling fluids without complex procedures. The testing process shows that the
average percentage error was found to be approximately 2% for the prediction of
shear stress values. Hence, rheological parameters of the drilling fluid could
be determined quickly and controllability was facilitated using artificial
neural network structure developed. 

References

  • 1. Bourgoyne, A.T., M.E. Chenevert, K.K. Millheim, and F.S. Young, 1991, Applied drilling engineering. SPE Textbook Series, vol. 2. Richardson, TX.
  • 2. Rooki, R., Ardejani, F. D., Moradzadeh, A., Mirzaei, H., Kelessidis, V., Maglione, R., and M. Norouzi, Optimal determination of rheological parameters for herschel-bulkley drilling fluids using genetic algorithms (GAs). Korea-Australia Rheology Journal, 2012. 24(3): p. 163-170.
  • 3. Elkatatny, S., Real-Time Prediction of Rheological Parameters of KCl Water-Based Drilling Fluid Using Artificial Neural Networks. Arabian Journal for Science and Engineering, 2017. 42(4): p. 1655-1665.
  • 4. Ali, J.K., Neural networks: a new tool for the petroleum industry., Paper SPE 27561 Presented at the 1994 European Computer Conference, Aberdeen, p. 15 – 17.
  • 5. González, A., Barrufet, M. A., and R. Startzman, Improved neural-network model predicts dewpoint pressure of retrograde gases. Journal of Petroleum Science and Engineering, 2003. 37(3): p. 183-194.
  • 6. Mohaghegh, S., Neural network: what it can do for petroleum engineers. Journal of Petroleum Technology, 1995. 47(01): p. 42-42.
  • 7. Mohaghegh, S., Virtual-intelligence applications in petroleum engineering: Part 1—Artificial neural networks. Journal of Petroleum Technology, 2000. 52(09): p. 64-73.
  • 8. Elkatatny, S., Tariq, Z., and M. Mahmoud, Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box). Journal of Petroleum Science and Engineering, 2016. 146: p. 1202-1210.
  • 9. da Silva Bispo, V. D., Scheid, C. M., Calçada, L. A., and L. A. da Cruz Meleiro, Development of an ANN-based soft-sensor to estimate the apparent viscosity of water-based drilling fluids. Journal of Petroleum Science and Engineering, 2017. 150: p. 69-73.
  • 10. Fann Instrument Company, Available from: http://www.fann.com/public1/pubsdata/Brochures/Mixers2.pdf
  • 11. Fann Instrument Company, Available from: http://www.fann.com/public1/pubsdata/Brochures/Rheometer_Model_50.pdf
  • 12. Ağyar, Z., Yapay sinir ağlarının kullanım alanları ve bir uygulama, Mühendis ve Makine Dergisi, 2015. 56(662): p. 22-23.
  • 13. Ataseven, B., Yapay sinir ağları ile öngörü modellemesi, 2013.Öneri Dergisi, 10(39), p. 101-115.
  • 14. Rooki, R., Estimation of pressure loss of Herschel–Bulkley drilling fluids during horizontal annulus using artificial neural network. Journal of Dispersion Science and Technology, 2015. 36(2): p. 161-169.
  • 15. Haykin, S., 1999, Neural Networks: A Comprehensive Foundation, 2nd ed.; Upper Saddle River, NJ: Prentice Hall
  • 16. Beale, M. H., M. T. Hagan,. and H. B. Demuth, Neural Network Toolbox™ User's Guide. The Mathworks Inc, 2017.
  • 17. Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., Van Dongen, B.F., Kindler, E. and C.W. Günther, Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 2010. 9(1): p. 87–111.
  • 18. Hussain, Q. E., and Sharif, M. A. R. Numerical modeling of helical flow of viscoplastic fluids in eccentric annuli, 2000. AIChE journal, 46(10), 1937-1946.
  • 19. Specht, L. P., Khatchatourian, O., Brito, L. A. T., and Ceratti, J. A. P. Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks. Materials Research, 2007. 10(1), 69-74.
  • 20. Elkatatny, S., Tariq, Z., and Mahmoud, M. Real time prediction of drilling fluid rheological properties using artificial neural networks visible mathematical model (white box). 2016. Journal of Petroleum Science and Engineering, 146, 1202-1210.
  • 21. Avcı, E., Effects of the geothermal water based muds on the drilling performance, Master Thesis, İskenderun Technical University, Institute of Science, 2018 (in Turkish, unpublished).
There are 21 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Emine Avcı This is me

Publication Date August 15, 2018
Submission Date March 14, 2018
Acceptance Date May 23, 2018
Published in Issue Year 2018 Volume: 2 Issue: 2

Cite

APA Avcı, E. (2018). An Artificial Neural Network Approach for the prediction of Water-Based Drilling Fluid Rheological Behaviour. International Advanced Researches and Engineering Journal, 2(2), 124-131.
AMA Avcı E. An Artificial Neural Network Approach for the prediction of Water-Based Drilling Fluid Rheological Behaviour. Int. Adv. Res. Eng. J. August 2018;2(2):124-131.
Chicago Avcı, Emine. “An Artificial Neural Network Approach for the Prediction of Water-Based Drilling Fluid Rheological Behaviour”. International Advanced Researches and Engineering Journal 2, no. 2 (August 2018): 124-31.
EndNote Avcı E (August 1, 2018) An Artificial Neural Network Approach for the prediction of Water-Based Drilling Fluid Rheological Behaviour. International Advanced Researches and Engineering Journal 2 2 124–131.
IEEE E. Avcı, “An Artificial Neural Network Approach for the prediction of Water-Based Drilling Fluid Rheological Behaviour”, Int. Adv. Res. Eng. J., vol. 2, no. 2, pp. 124–131, 2018.
ISNAD Avcı, Emine. “An Artificial Neural Network Approach for the Prediction of Water-Based Drilling Fluid Rheological Behaviour”. International Advanced Researches and Engineering Journal 2/2 (August 2018), 124-131.
JAMA Avcı E. An Artificial Neural Network Approach for the prediction of Water-Based Drilling Fluid Rheological Behaviour. Int. Adv. Res. Eng. J. 2018;2:124–131.
MLA Avcı, Emine. “An Artificial Neural Network Approach for the Prediction of Water-Based Drilling Fluid Rheological Behaviour”. International Advanced Researches and Engineering Journal, vol. 2, no. 2, 2018, pp. 124-31.
Vancouver Avcı E. An Artificial Neural Network Approach for the prediction of Water-Based Drilling Fluid Rheological Behaviour. Int. Adv. Res. Eng. J. 2018;2(2):124-31.



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