Skip to main content

Advertisement

SpringerLink
Log in

Estimation of cementation factor in carbonate reservoir by using genetic fuzzy inference system

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Water saturation is a key parameter in reservoir engineering to calculate the volume of hydrocarbon in reservoirs. The first attempt to estimate water saturation using well log data was implemented by Archie in 1942 for a clean sandstone reservoir. This method requires laboratory measurement of cementation factor. This parameter has the most impression on water saturation calculation compared to the other parameters, which is nearly constant in homogenous sandstone reservoirs. However, due to high variation of cementation factor along depth of wellbore in carbonate reservoirs due to rock’s nature, it is incorrect to assign a constant value to cementation factor. On the other hand, experimental core analysis to determine cementation factor values is an expensive and time-consuming work, and it is impossible to calculate this parameter in laboratory for the whole depth of a drilled wellbore. In industrial applications, using a constant cementation factor can lead to erroneous calculations of water (and hence oil) saturations. Also, previous conventional methods estimating cementation factor from logging data are not often sensitive to pore system of rock and generate a massive source of error. In this study, a new approach to estimate cementation factor using a genetic Mamdani fuzzy inference system was implemented for a case study in Sarvak Formation located in Zagros Basin which is mostly composed of pure limestone. The final results show a high exactness of the proposed model estimating cementation factor with an R-squared of 0.864 and a mean squared error of 0.01899 .

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

m :

Cementation factor

φ :

Porosity

a :

Tortuosity factor

n :

Stauration exponent

FIS:

Fuzzy inference system

FRF:

Formation resistivity factor

R 2 :

R-squared

MSE:

Mean square error

k :

Permeability

MF:

Membership function

c :

Center of Gaussian and sigmoidal membership function

σ :

Width of Gaussian membership function

α :

Parameter of sigmoid membership function

GA:

Genetic algorithm

MD:

Membership degree

FMI:

Formation micro imager

NMR:

Nuclear magnetic resonance

References

  1. Archie GE (1942) The electrical resistivity log as an aid in determining some reservoir characteristics. Trans AIME 146:54–67

    Article  Google Scholar 

  2. Winsauer WO, Shearin HM, Masson PH, Williams M (1952) Resistivity of brine saturated sands in relation to pore geometry. Am Assoc Pet Geol Bull 36:253–277

    Google Scholar 

  3. Ransom RC (1984) A contribution toward a better understanding of the modified Archie formation resistivity factor relationship. Log Anal 2:7–12

    Google Scholar 

  4. Salem HS, Chilingarian GV (1990) The cementation factor of Archie’s equations for shaly sandstone reservoirs. J Pet Sci Eng 23:83–93

    Article  Google Scholar 

  5. Doveton JH (2014) Principle of mathematical petrophysics. International Association for Mathematical Geoscience, Oxford University Press, Oxford

    MATH  Google Scholar 

  6. Hamada GM, Almajed AA, Okasha TM, Alghate AA (2013) Uncertainty of Archie’s parameters determination techniques in carbonate reservoirs. J Pet Explor Prod Technol 3:1–10

    Article  Google Scholar 

  7. Salazar JM, Wang GL, Verdin CT, Lee HJ (2007) Combined simulation and inversion of SP and resistivity logs for the estimation of connate water resistivity and Archie’s cementation exponent. In: SPWLA, Austin, TX

  8. Schlumberger: log interpretation charts 15. Huston (1984)

  9. Borai AM (1987) A new correlation for the cementation factor in low-porosity carbonates. SPE Form Eval 2(4):495–499

    Article  Google Scholar 

  10. Sethi DK (1979) Some considerations about the formation consideration resistivity factor-porosity relations. In: SPWLA, 20th annual logging symposium

  11. Towel G (1962) An analysis of the formation resistivity factor-porosity relationship of some assumed pore geometries. In: SPWLA 3rd annual logging symposium

  12. Focke JW, Munn D (1987) Cementation exponent in Middle Eastern carbonate reservoirs. SPE Paper No. 13737, SPE formation evaluation, June, a55–167

  13. Gomez-Rivero O (1977) Some consideration about the possible use of the parameters a and m as a formation evaluation tool through well logs. In: SPWLA 18th annual logging symposium

  14. Cuddy SJ, Glover PWJ (2002) The application of fuzzy logic and genetic algorithms to reservoir characterization and modeling. Fuzziness Soft Comput 80:219–241

    Article  Google Scholar 

  15. Potter DK et al (2003) Genetic petrophysics approach to core analysis-application to shoreface sandstone reservoirs. In: International symposium of the society of core analysts, SCA2005-37. Pau, France

  16. Le AH, Potter DK (2003) Generally focused neural nets for permeability prediction from wireline logs. In: 65th conference of European association of geoscience and engineers. Stavanger, Norway

  17. Cordon S, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems evaluation tuning and learning of fuzzy knowledge bases. World Scientific Publishing Co, Pte, Ltd, Hackensack

    Book  MATH  Google Scholar 

  18. Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 3:28–44

    Article  MathSciNet  MATH  Google Scholar 

  19. Matlab User’s Guide (2007) Fuzzy logic toolbox user’s guide Matlab CD-ROM. MathWorks Inc, Natick

    Google Scholar 

  20. Holland JH (1975) Adaptation in natural and artificial system. University of Michigan Press, Ann Arbor, p 183

    Google Scholar 

  21. Farag WA, Quintana VH, Lambert-Torres G (1998) A genetic-based Neuro-Fuzzy approach for modeling and control of dynamical systems. IEEE Trans Neural Netw 9(b):756–767

    Article  Google Scholar 

  22. Lucia FJ (1983) Petrophysical parameters estimated from visual descriptions of carbonate rocks: a field classification of carbonate pore space. J Pet Technol 35:629–638

    Article  Google Scholar 

  23. Homaifar A, McCormick E (1995) Simultaneous design of membership functions and rule sets for fuzzy controller using genetic algorithm. IEEE Trans Fuzzy Syst 3(2):129–139

    Article  Google Scholar 

  24. Marsh S, Huang YW, Sibigtroth J (1994) Center of emerging compute technologies, Motorola. Fuzzy Logic Program 2.0

  25. Miller BL (1995) Genetic algorithm, tournament selection, and the effect of noise. Complex Syst 9:193–212

    MathSciNet  Google Scholar 

  26. Sywerda G (1989) Uniform crossover in genetic algorithms. In: Proceedings of the 3rd international conference on genetic algorithm, USA, pp 2–9

  27. Leung FHF, Lam HK, Ling SH, Tam PKS (2003) Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Netw 14:79–88

    Article  Google Scholar 

  28. Masoudi P, Tokhmechi B, Bashari A, Jafari MA (2012) Identification productive zones of the Sarvak formation by integrating outputs of different classification methods. J Geophys Eng 9:282–290

    Article  Google Scholar 

  29. Coates GR, Xiao L, Prammer MG (1999) NMR logging, principle and applications. Halliburton Energy Services Publication Inc., Huston, pp 64–66

    Google Scholar 

  30. Shafer JL, Chen S, Georgi DT (2005) Protocols for calibrating NMR log-derived permeabilities. In: International symposium of the society of core analysts, SCA2005-37

  31. Allen D et al (2000) Trends in NMR logging. Oilfield Rev 12:2–19

    Google Scholar 

  32. Delhomme JP (2007) The quest for permeability evaluation in wireline logging. Oilfield Rev 8(1):10–12

    Google Scholar 

  33. Serra O (1989) Formation micro scanner image interpretation. Schlumberger Educational Services, Houston, pp 80–83

    Google Scholar 

  34. Sullivan KB, Schepel KJ (1995) Borehole image logs: applications in fractured and thinly bedded reservoirs. In: SPWLA 36th annual logging symposium

  35. Albert BY, Frohne KH, Komar CA, Ameri S (1982) Techniques to determine natural and induced fracture relationships in Devonian shale. J Pet Technol 34:1371–1377

    Article  Google Scholar 

  36. Hornby BE, Luthi SM (1992) An integrated interpretation of fracture apertures computed from electrical borehole scans and reflected Stoneley waves. Geological application of wireline logs II. Geol Soc Spec Appl 65:185–198

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Petroleum Engineering, Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran

    Hamid Heydari Gholanlo

  2. Department of Chemical Engineering, Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran

    Zohreh Hajipour

Authors
  1. Hamid Heydari Gholanlo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zohreh Hajipour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Heydari Gholanlo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Heydari Gholanlo, H., Hajipour, Z. Estimation of cementation factor in carbonate reservoir by using genetic fuzzy inference system. Neural Comput & Applic 30, 1657–1666 (2018). https://doi.org/10.1007/s00521-016-2770-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2770-1

Keywords

Search

Navigation