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New Model for Pore Pressure Prediction While Drilling Using Artificial Neural Networks

  • Abdulmalek Ahmed
  • Salaheldin Elkatatny
  • Abdulwahab Ali
  • Mohamed Mahmoud
  • Abdulazeez Abdulraheem
Research Article - Petroleum Engineering
  • 21 Downloads

Abstract

Pore pressure is one of the main formation conditions that affects the efficiency of drilling operations and impacts its cost. Accurate prediction of the pore pressure and the parameters controlling it will help reduce the drilling cost and avoid in some cases catastrophic accidents. Many empirical models reported in the literature were used to predict the pore pressure based on either drilling parameters or log data. Empirical models require trends such as normal or abnormal pressure to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the pore pressure using one or maximum two AI methods (which are black box). There is no developed empirical correlation for pore pressure prediction based on optimized AI techniques. The objective of this paper is to predict the pore pressure based on both drilling parameters and log data, namely weight on bit (WOB), rotary speed (RPM), rate of penetration (ROP), mud weight (MW), bulk density (RHOB), porosity (\(\phi \)), and compressional time (\(\Delta {t}\)). Real field data will be used to predict the pore pressure using artificial neural network (ANN). Finally, for the first time, a new empirical correlation for pore pressure prediction will be developed based on the optimized ANN model. The obtained results showed that it is very important to combine the drilling parameters and the logging data to predict the pore pressure with a high accuracy. A new empirical correlation was developed using the optimized ANN method that can estimate pore pressure with high accuracy (correlation coefficient of 0.998 and average absolute percentage error of 0.17%). Unlike the published empirical models, the new model requires no prior pressure trends (such as normal or abnormal pressures) to perform prediction.

Keywords

Pore pressure Artificial neural network Drilling parameters Empirical correlation 

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References

  1. 1.
    Hu, L.; Deng, J.; Zhu, H.; Lin, H.; Chen, Z.; Deng, F.; Yan, C.: A new pore pressure prediction method-back propagation artificial neural network. Res. Gate 18(18), 4093–4107 (2013)Google Scholar
  2. 2.
    Keshavarzi,; Jahanbakhshi, : Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields. Eur. J. Environ. Civ. Eng. 17(8), 675–686 (2013)CrossRefGoogle Scholar
  3. 3.
    Mitchell, R.L.; Miska, S.Z.; Aadnoy, B.S.: Fundamentals of Drilling Engineering. Society of Petroleum Engineers, Richardson (2011)Google Scholar
  4. 4.
    Adams, N.J.: Drilling Engineering: A Complete Well Planning Approach. Pennwell, Tulsa (1985)Google Scholar
  5. 5.
    Wang, Z.; Wang, R.: Pore pressure prediction using geophysical methods in carbonate reservoirs: current status, challenges and way ahead. J. Nat. Gas Sci. Eng. 27, 986–993 (2015)CrossRefGoogle Scholar
  6. 6.
    Hossain, M.E.; Al-Majed, A.A.: Fundamentals of Sustainable Drilling Engineering. Scrivener Publishing LLC, Hoboken (2015)Google Scholar
  7. 7.
    Bourgoyne, A.T.; Chenevert, M.E.; Millheim, K.K.; Young Jr., : Applied Drilling Engineering, vol. 2. SPE Textbook Series, New York (1991)Google Scholar
  8. 8.
    Mouchet, J.P.; Mitchell, A.: Abnormal Pressure While Drilling. Manuals Techniques 2. Elf Aquitaine Editions, Boussens (1989)Google Scholar
  9. 9.
    Hottmann, C.E.; Johnson, R.K.: Estimation of formation pressures from log-derived shale properties. Soc. Pet. Eng. (1965).  https://doi.org/10.2118/1110-PA CrossRefGoogle Scholar
  10. 10.
    Bingham M.G.: A new approach to interpreting rock drillability. Re-printed from Oil Gas J. (1965)Google Scholar
  11. 11.
    Jorden, J.R.; Shirley, O.J.: Application of drilling performance data to overpressure detection. Soc. Pet. Eng. (1966).  https://doi.org/10.2118/1407-PA CrossRefGoogle Scholar
  12. 12.
    Matthews, W.R.; Kelly, J.: How to predict formation pressure and fracture gradient. Oil Gas J. 65, 92–1066 (1967)Google Scholar
  13. 13.
    Pennebaker, E.S.: Detection of Abnormal-Pressure Formation from Seismic Field Data. American Petroleum Institute, Washington (1968)Google Scholar
  14. 14.
    Rehm, B.; McClendon, R.: Measurement of formation pressure from drilling data. Soc. Pet. Eng. (1971).  https://doi.org/10.2118/3601-MS CrossRefGoogle Scholar
  15. 15.
    Zamora, M.; Lord, D.L.: Practical analysis of drilling mud flow in pipes and annuli. Soc. Pet. Eng. (1974).  https://doi.org/10.2118/4976-MS CrossRefGoogle Scholar
  16. 16.
    Eaton, B.A.: The effect of overburden stress on geopressure prediction from well logs. Soc. Pet. Eng. (1972).  https://doi.org/10.2118/3719-PA CrossRefGoogle Scholar
  17. 17.
    Eaton, B.A.: The equation for geopressure prediction from well logs. Soc. Pet. Eng. (1975).  https://doi.org/10.2118/5544-MS CrossRefGoogle Scholar
  18. 18.
    Foster, J.B.: Estimation of formation pressures from electrical surveys-offshore Louisiana. Soc. Pet. Eng. (1966).  https://doi.org/10.2118/1200-PA CrossRefGoogle Scholar
  19. 19.
    Swarbrick, R.: Challenges of porosity-based pore pressure prediction. Can. Soc. Explor. Geophys. 27(07), 1–8 (2002)Google Scholar
  20. 20.
    Ifeanyi, N.R.: Comparing methods of predicting pore pressure. Int. J. Sci. Eng. Res. 6(6), 2229–5518 (2015). issnGoogle Scholar
  21. 21.
    Pranavathiyani G.: What is Artificial Intelligence (AI)? Retrieved from Mar 29, 2018. https://towardsdatascience.com/what-is-artificial-intelligence-ai-ad5ba87b55dd (2017)
  22. 22.
    Elkatatny, S.M.; Zeeshan, T.; Mahmoud, M.A.: Real time prediction of drilling fluid rheological properties using artificial neural networks visible mathematical model (white box). J. Pet. Sci. Eng. 146, 1202–1210 (2016)CrossRefGoogle Scholar
  23. 23.
    Elkatatny, S.M.: Real time prediction of rheological parameters of KCl water-based drilling fluid using artificial neural networks. Arab. J. Sci. Eng. 42(4), 1655–1665 (2017a)CrossRefGoogle Scholar
  24. 24.
    Elkatatny, S.M.: New approach to optimize the rate of penetration using artificial neural network. Arab. J. Sci. Eng. (2017b).  https://doi.org/10.1007/s13369-017-3022-0
  25. 25.
    Mousa, T.; Elkatatny, S.M.; Mahmoud, M.A.; Abdulraheem, A.: Improved permeability correlations from well log data using artificial intelligence approaches. J. Energy Resour. Technol. 140(7) (2018).  https://doi.org/10.1115/1.4039270 CrossRefGoogle Scholar
  26. 26.
    Elkatatny, S.M.; Mahmoud, M.A.; Zeeshan, T.; Abdulraheem, A.: New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligent network. Neural Comput. Appl. (2017c).  https://doi.org/10.1007/s00521-017-2850-x
  27. 27.
    Elkatatny, S.M.; Zeeshan, T.; Mahmoud, M.A.; Abdulraheem, A.: New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs. Petroleum (2018a).  https://doi.org/10.1016/j.petlm.2018.04.002
  28. 28.
    Abdulhameed, A.; Elkatatny, S.M.; Mahmoud, M.A.; Aburesh, M.; Abdulraheem, A.; Ali, A.: Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network. Int. J. Coal Geol. 179, 72–80 (2017)CrossRefGoogle Scholar
  29. 29.
    Elkatatny, S.M.; Mahmoud, M.: Development of new correlations for the oil formation volume factor in oil reservoirs using artificial intelligent white box technique. Petroleum 4, 178–186 (2017d)CrossRefGoogle Scholar
  30. 30.
    Tariq, Z.; Al-Nuaim, S.; Abdulraheem, A.; Khan, M.R.: New methodology to quantify productivity of vertical wells in naturally fractured solution gas drive reservoirs with dual porosity and dual permeability. Soc. Pet. Eng. (2016).  https://doi.org/10.2118/185314-MS CrossRefGoogle Scholar
  31. 31.
    Elkatatny, S.M.; Zeeshan, T.; Mahmoud, M.A.; Abdulraheem, A.; Mohamed, I.: An integrated approach for estimating static Young’s modulus using artificial intelligence tools. Neural Comput. Appl. (2018b).  https://doi.org/10.1007/s00521-018-3344-1
  32. 32.
    Elkatatny, S.M.: Application of artificial intelligence techniques to estimate the static Poisson’s ratio based on wireline log data. J. Energy Resour. Technol. 140, 072905 (2018c)CrossRefGoogle Scholar
  33. 33.
    Tariq, Z.; Elkatatny, S.M.; Mahmoud, M.A.; Abdulraheem, A.; Abdelwahab, A.Z.; Woldeamanuel, M.; Mohamed, I.M.: Development of new correlation of unconfined compressive strength for carbonate reservoir using artificial intelligence techniques. In: Paper ARMA-2017-0428 presented at the 51st U.S. Rock Mechanics/Geomechanics Symposium, 25–28 June, San Francisco, California, USA. American Rock Mechanics Association (2017a)Google Scholar
  34. 34.
    Tariq, Z.; Elkatatny, S.; Mahmoud, M.; Ali, A.Z.; Abdulraheem, A.: A new approach to predict failure parameters of carbonate rocks using artificial intelligence tools. Soc. Pet. Eng. (2017b).  https://doi.org/10.2118/187974-MS CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Abdulmalek Ahmed
    • 1
  • Salaheldin Elkatatny
    • 1
    • 2
  • Abdulwahab Ali
    • 3
  • Mohamed Mahmoud
    • 1
  • Abdulazeez Abdulraheem
    • 1
  1. 1.Department of Petroleum EngineeringKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia
  2. 2.Petroleum DepartmentCairo UniversityCairoEgypt
  3. 3.Center for Integrative Petroleum Research (CIPR)King Fahd University of Petroleum and MineralsDhahranSaudi Arabia

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