Skip to main content
SpringerLink
Log in

Estimation of P- and S-wave impedances using Bayesian inversion and adaptive neuro-fuzzy inference system from a carbonate reservoir in Iran

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

Abstract

P- and S-wave impedances are accounted as two significant parameters conventionally inverted from seismic amplitudes for evaluation of gas and oil reservoirs. They may not be the final goal of interpretation studies; however, they play an important role in many methods such as reservoir characterization, rock physical modeling, geostatistical simulation, fluid detection. Bayesian inversion is a conventional method used by many researchers and even by industry to invert these parameters. To compare this method with intelligent methods, the adaptive network-based fuzzy inference system (ANFIS) was utilized to construct a model for the prediction of P- and S-wave impedances. Two ANFIS models were implemented, subtractive clustering method (SCM) and fuzzy c-means clustering method. The prediction capabilities offered by ANFIS models were shown by using field data obtained from a carbonate reservoir in Iran. Unlike other studies, input parameters, in this study, are pre-stack seismic data and attributes, while the P- and S-wave impedances are the output parameters in all methods. Mean square error was used for comparison of the performance of those models. The obtained results show that the ANFIS-SCM model generates the best indirect estimation of P- and S-wave impedances with high degree of accuracy and robustness.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Farfour M, Yoon WJ, Kim J (2015) Seismic attributes and acoustic impedance inversion in interpretation of complex hydrocarbon reservoirs. J Appl Geophys 114:68–80

    Article  Google Scholar 

  2. Karimpouli S, Hassani H, Nabi-Bidhendi M, Khoshdel H, Malehmir A (2013) Application of probabilistic facies prediction and estimation of rock physics parameters in a carbonate reservoir from Iran. J Geophys Eng 10(1):015008

    Article  Google Scholar 

  3. González EF, Mukerji T, Mavko G (2007) Seismic inversion combining rock physics and multiple-point geostatistics. Geophysics 73(1):R11–R21

    Article  Google Scholar 

  4. Tahmasebi P, Sahimi M (2015) Geostatistical simulation and reconstruction of porous media by a cross-correlation function and integration of hard and soft data. Transp Porous Med 107(3):871–905

    Article  Google Scholar 

  5. Malehmir A, Durrheim R, Bellefleur G, Urosevic M, Juhlin C, White DJ, Milkereit B, Campbell G (2012) Seismic methods in mineral exploration and mine planning: a general overview of past and present case histories and a look into the future. Geophysics 77(5):173–190

    Article  Google Scholar 

  6. Khalid P, Ahmed N, Mahmood A, Saleem MA (2015) An integrated seismic interpretation and rock physics attribute analysis for pore fluid discrimination. Arab J Sci Eng 41(1):1–10

    Google Scholar 

  7. Duijndam A (1988) Bayesian estimation in seismic inversion. Part i: PRINCIPLES1. Geophys Prospect 36(8):878–898

    Article  Google Scholar 

  8. Ulrych TJ, Sacchi MD, Woodbury A (2001) A Bayes tour of inversion: a tutorial. Geophysics 66(1):55–69

    Article  Google Scholar 

  9. Tarantola A (2005) Inverse problem theory and methods for model parameter estimation. SIAM, Philadelphia

    Book  MATH  Google Scholar 

  10. Buland A, Omre H (2003) Bayesian linearized AVO inversion. Geophysics 68(1):185–198

    Article  Google Scholar 

  11. Karimpouli S, Malehmir A (2015) Neuro-Bayesian facies inversion of prestack seismic data from a carbonate reservoir in Iran. J Pet Sci Eng 131:11–17

    Article  Google Scholar 

  12. Zhao L, Geng J, Cheng J, D-h Han, Guo T (2014) Probabilistic lithofacies prediction from prestack seismic data in a heterogeneous carbonate reservoir. Geophysics 79(5):M25–M34

    Article  Google Scholar 

  13. Grana D, Della Rossa E (2010) Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion. Geophysics 75(3):O21–O37

    Article  Google Scholar 

  14. Buland A, Kolbjørnsen O, Hauge R, Skjæveland Ø, Duffaut K (2008) Bayesian lithology and fluid prediction from seismic prestack data. Geophysics 73(3):13–21

    Article  Google Scholar 

  15. Karimpouli S, Hassani H, Khoshdel H, Malehmir A, Nabi-Bidhendi M (2014) Detection of high quality parts of hydrocarbon reservoirs using bayesian facies estimation: a case study on a carbonate reservoir from Iran. Advances in data, methods, models and their applications in oil/gas exploration, pp 93–130

  16. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE T Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  17. Fattahi H, Karimpouli S (2016) Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods. Comput Geosci 20(5):1–20

    Article  MathSciNet  MATH  Google Scholar 

  18. Rezaee MR, Kadkhodaie Ilkhchi A, Barabadi A (2007) Prediction of shear wave velocity from petrophysical data utilizing intelligent systems: an example from a sandstone reservoir of Carnarvon Basin, Australia. J Pet Sci Eng 55(3):201–212

    Article  Google Scholar 

  19. Zoveidavianpoor M, Samsuri A, Shadizadeh SR (2013) Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir. J Appl Geophys 89:96–107

    Article  Google Scholar 

  20. Ansari HR (2014) Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir. J Appl Geophys 108:61–68

    Article  Google Scholar 

  21. Golsanami N, Kadkhodaie-Ilkhchi A, Erfani A (2015) Synthesis of capillary pressure curves from post-stack seismic data with the use of intelligent estimators: a case study from the Iranian part of the South Pars gas field, Persian Gulf Basin. J Appl Geophys 112:215–225

    Article  Google Scholar 

  22. Rajabi M, Bohloli B, Gholampour Ahangar E (2010) Intelligent approaches for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: a case study from the Sarvak carbonate reservoir in the Abadan Plain (Southwestern Iran). Comput Geosci 36(5):647–664

    Article  Google Scholar 

  23. Asoodeh M, Bagheripour P (2012) Prediction of compressional, shear, and stoneley wave velocities from conventional well log data using a committee machine with intelligent systems. Rock Mech Rock Eng 45(1):45–63

    Article  Google Scholar 

  24. Mojeddifar S, Kamali G, Ranjbar H, Salehipour Bavarsad B (2014) A comparative study between a pseudo-forward equation (PFE) and intelligence methods for the characterization of the north sea reservoir. Int J Min Geo Eng 48(2):173–190

    Google Scholar 

  25. Aki K, Richards PG (2002) Quantitative seismology, vol 1. W H Freeman & Co., San Francisco

    Google Scholar 

  26. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278

    Google Scholar 

  27. Bezdek JC (1973) Fuzzy mathematics in pattern classification. Cornell University, Ithaca

    Google Scholar 

  28. Malehmir A, Juhlin C, Wijns C, Urosevic M, Valasti P, Koivisto E (2012) 3D reflection seismic imaging for open-pit mine planning and deep exploration in the Kevitsa Ni-Cu-PGE deposit, northern Finland. Geophysics 77(5):95–108

    Article  Google Scholar 

  29. Backus G (1962) Long-wave elastic anisotropy reduced by horizontal layering. J Geophys Res 67:4427–4440

    Article  MATH  Google Scholar 

  30. Barnes AE (1996) Theory of 2-D complex seismic trace analysis. Geophysics 61(1):264–272

    Article  MathSciNet  Google Scholar 

  31. Tahmasebi P, Hezarkhani A (2011) Application of a modular feedforward neural network for grade estimation. Nat Resour Res 20(1):25–32

    Article  Google Scholar 

  32. Tahmasebi P, Hezarkhani A (2010) Comparison of optimized neural network with fuzzy logic for ore grade estimation. Aust J Basic Appl Sci 4(5):764–772

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mining Engineering Group, Faculty of Engineering, University of Zanjan, Zanjan, Iran

    Sadegh Karimpouli

  2. Department of Mining Engineering, Arak University of Technology, Arāk, Iran

    Hadi Fattahi

Authors
  1. Sadegh Karimpouli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hadi Fattahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hadi Fattahi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karimpouli, S., Fattahi, H. Estimation of P- and S-wave impedances using Bayesian inversion and adaptive neuro-fuzzy inference system from a carbonate reservoir in Iran. Neural Comput & Applic 29, 1059–1072 (2018). https://doi.org/10.1007/s00521-016-2636-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2636-6

Keywords

Search

Navigation