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Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods

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Abstract

Rock physical parameters such as porosity and water saturation play an important role in the mechanical behavior of hydrocarbon reservoir rocks. A valid and reliable prediction of these parameters from seismic data is essential for reservoir characterization, management, and also geomechanical modeling. In this paper, the application of conventional methods such as Bayesian inversion and computational intelligence methods, namely support vector regression (SVR) optimized by particle swarm optimization (PSO) and adaptive network-based fuzzy inference system-subtractive clustering method (ANFIS-SCM), is demonstrated to predict porosity and water saturation. The prediction abilities offered by Bayesian inversion, SVR-PSO, and ANFIS-SCM were presented using a synthetic dataset and field data available from a gas carbonate reservoir in Iran. In these models, seismic pre-stack data and attributes were utilized as the input parameters, while the porosity and water saturation were the output parameters. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-SCM model has strong potential for indirect estimation of porosity and water saturation with high degree of accuracy and robustness from seismic data and attributes in both synthetic and real cases of this study.

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References

  1. Zoback, M.D.: Reservoir Geomechanics. Cambridge University Press, New York (2007)

    Book  Google Scholar 

  2. Sengupta, M., Dai, J., Volterrani, S., Dutta, N., Rao, N.S., Al-Qadeeri, B., Kidambi, V.K.: Building a seismic-driven 3D geomechanical model in a deep carbonate reservoir. In: SEG Annual Meeting 2011 Society of Exploration Geophysicists (2011)

  3. Herwanger, J.: Seismic geomechanics: how to build and calibrate geomechanical models using 3D and 4D seismic data. In: Education Days Stavanger 2014 (2014)

  4. Hampson, D.P., Russell, B.H., Bankhead, B.: Simultaneous inversion of pre-stack seismic data. In: SEG Annual Meeting 2005 Society of Exploration Geophysicists (2005)

  5. Kadkhodaie-Ilkhchi, R., Moussavi-Harami, R., Rezaee, R., Nabi-Bidhendi, M., Kadkhodaie-Ilkhchi, A.: Seismic inversion and attributes analysis for porosity evaluation of the tight gas sandstones of the Whicher Range field in the Perth Basin, Western Australia. J. Natu. Gas. Sci. Eng. 21, 1073–1083 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Boadu, F.K.: Rock properties and seismic attenuation: neural network analysis. Pure. Appl. Geophys. 149(3), 507–524 (1997)

    Article  Google Scholar 

  9. Aminzadeh, F., Barhen, J., Glover, C., Toomarian, N.: Reservoir parameter estimation using a hybrid neural network. Comput. Geosci. 26(8), 869–875 (2000)

    Article  Google Scholar 

  10. Nikravesh, M., Adams, R.D., Levey, R.A.: Soft computing: tools for intelligent reservoir characterization (IRESC) and optimum well placement (OWP). J. Pet. Sci. Eng. 29(3), 239–262 (2001)

    Article  Google Scholar 

  11. Lim, J.-S.: Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. J. Pet. Sci. Eng. 49(3), 182–192 (2005)

    Article  Google Scholar 

  12. Kadkhodaie-Ilkhchi, A., Rezaee, M.R., Rahimpour-Bonab, H., Chehrazi, A.: Petrophysical data prediction from seismic attributes using committee fuzzy inference system. Comput. Geosci. 35(12), 2314–2330 (2009)

    Article  Google Scholar 

  13. Karimpouli, S., Fathianpour, N., Roohi, J.: A new approach to improve neural networks’ algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN). J. Pet. Sci. Eng. 73(3), 227–232 (2010)

    Article  Google Scholar 

  14. Al-Dousari, M.M., Garrouch, A.A.: An artificial neural network model for predicting the recovery performance of surfactant polymer floods. J. Pet. Sci. Eng. 109, 51–62 (2013)

    Article  Google Scholar 

  15. Iturrarán-Viveros, U., Parra, J.O.: Artificial neural networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data. J. Appl. Geophys. 107, 45–54 (2014)

    Article  Google Scholar 

  16. Na’imi, S., Shadizadeh, S., Riahi, M., Mirzakhanian, M.: Estimation of reservoir porosity and water saturation based on seismic attributes using support vector regression approach. J. Appl. Geophys. 107, 93–101 (2014)

    Article  Google Scholar 

  17. Nooruddin, H.A., Anifowose, F., Abdulraheem, A.: Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density. Comput. Geosci. 64, 72–80 (2014)

    Article  Google Scholar 

  18. Verma, A.K., Chaki, S., Routray, A., Mohanty, W.K., Jenamani, M.: Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach. J. Appl. Geophys. 111, 141–155 (2014)

    Article  Google Scholar 

  19. Aleardi, M.: Seismic velocity estimation from well log data with genetic algorithms in comparison to neural networks and multilinear approaches. J. Appl. Geophys. 117, 13–22 (2015)

    Article  Google Scholar 

  20. Chaki, S., Routray, A., Mohanty, W.K.: A Novel preprocessing scheme to improve the prediction of sand fraction from seismic attributes using neural networks. IEEE J. Sel. Top. Appl. 8, 1808–1820 (2015)

    Google Scholar 

  21. Golsanami, N., Kadkhodaie-Ilkhchi, A., Erfani, A.: 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 (2015)

    Article  Google Scholar 

  22. Nourafkan, A., Kadkhodaie-Ilkhchi, A.: Shear wave velocity estimation from conventional well log data by using a hybrid ant colony–fuzzy inference system: a case study from Cheshmeh–Khosh oilfield. J. Pet. Sci. Eng. 127, 459–468 (2015)

    Article  Google Scholar 

  23. Sun, Y.F.: Pore structure effects on elastic wave propagation in rocks: AVO modelling. J. Geophys. Eng. 1(4), 268 (2004)

    Article  Google Scholar 

  24. Eberli, G.P., Baechle, G.T., Anselmetti, F.S., Incze, M.L.: Factors controlling elastic properties in carbonate sediments and rocks. Lead. Edge 22(7), 654–660 (2003)

    Article  Google Scholar 

  25. Taner, M.T., Koehler, F., Sheriff, R.: Complex seismic trace analysis. Geophysics 44(6), 1041–1063 (1979)

    Article  Google Scholar 

  26. Barnes, A.: Theory of 2D complex seismic trace analysis. Geophysics 61, 264–272 (1996)

    Article  Google Scholar 

  27. Karimpouli, S., Malehmir, A., Hassani, H., Khoshdel, H., Nabi-Bidhendi, M.: Automated diffraction delineation using an apex-shifted Radon transform. J. Geophys. Eng. 12(2), 199 (2015)

    Article  Google Scholar 

  28. Vapnik, V.: The Nature of Statistical Learning Theory. Springer (1999)

  29. Al-Anazi, A., Gates, I.: Support vector regression for porosity prediction in a heterogeneous reservoir: a comparative study. Comput. Geosci. 36(12), 1494–1503 (2010)

    Article  Google Scholar 

  30. Jiang, B., Zhao, F.: Combination of support vector regression and artificial neural networks for prediction of critical heat flux. Int. J. Heat. Mass. Tran. 62, 481–494 (2013)

    Article  Google Scholar 

  31. Wu, Q., Law, R.: Fuzzy support vector regression machine with penalizing Gaussian noises on triangular fuzzy number space. Expert. Syst. Appl. 37(12), 7788–7795 (2010)

    Article  Google Scholar 

  32. Gunn, S.R.: Support vector machines for classification and regression. ISIS technical report 14 (1998)

  33. Vapnik, V., Golowich, S.E., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. Adv. Neur. In., 281–287 (1997)

  34. Huang, C.-L., Dun, J.-F.: A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Appl. Soft. Comput. 8(4), 1381–1391 (2008)

    Article  Google Scholar 

  35. Huang, C.-L., Wang, C.-J.: A GA-based feature selection and parameters optimizationfor support vector machines. Expert. Syst. Appl. 31(2), 231–240 (2006)

    Article  Google Scholar 

  36. Sarafrazi, S., Nezamabadi-pour, H.: Facing the classification of binary problems with a GSA-SVM hybrid system. Math. Comput. Model. 57(1), 270–278 (2013)

    Article  Google Scholar 

  37. Ranaee, V., Ebrahimzadeh, A., Ghaderi, R.: Application of the PSO–SVM model for recognition of control chart patterns. ISA Trans. 49(4), 577–586 (2010)

    Article  Google Scholar 

  38. Friedrichs, F., Igel, C.: Evolutionary tuning of multiple SVM parameters. Neurocomputing 64, 107–117 (2005)

    Article  Google Scholar 

  39. Amari, S.-I., Wu, S.: Improving support vector machine classifiers by modifying kernel functions. Neural. Netw. 12(6), 783–789 (1999)

    Article  Google Scholar 

  40. Hong, W.-C.: Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy 36(9), 5568–5578 (2011)

    Article  Google Scholar 

  41. Wu, C.-H., Tzeng, G.-H., Lin, R.-H.: A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert. Syst. Appl. 36(3), 4725–4735 (2009)

    Article  Google Scholar 

  42. Chen, K.-Y., Wang, C.-H.: Support vector regression with genetic algorithms in forecasting tourism demand. Tourism. Manage. 28(1), 215–226 (2007)

    Article  Google Scholar 

  43. Zheng, L., Zhou, H., Wang, C., Cen, K.: Combining support vector regression and ant colony optimization to reduce NOx emissions in coal-fired utility boilers. Energ. Fuel. 22(2), 1034–1040 (2008)

    Article  Google Scholar 

  44. Hong, W.-C., Chen, Y.-F., Chen, P.-W., Yeh, Y.-H.: Continuous ant colony optimization algorithms in a support vector regression based financial forecasting model. In: Third International Conference on Natural Computation, 2007. ICNC 2007, pp. 548–552. IEEE (2007)

  45. Zheng, L., Yu, M.: Support vector regression and ant colony optimization for combustion performance of boilers. In: Fourth International Conference on Natural Computation, 2008. ICNC’08, pp. 178–182. IEEE (2008)

  46. Fattahi, H., Gholami, A., Amiribakhtiar, M.S., Moradi, S.: Estimation of asphaltene precipitation from titration data: a hybrid support vector regression with harmony search. Neural. Comput. Appl. 26, 789–798 (2014)

    Article  Google Scholar 

  47. Eberhart, R., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995. MHS’95, pp. 39-43. IEEE (1995)

  48. Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE (1998)

  49. Lin, C.T., Lee, C.S.G.: Neural-network-based fuzzy logic control and decision system. IEEE T. Comput. 40(12), 1320–1336 (1991)

    Article  Google Scholar 

  50. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE T. Syst. Man. Cyb. 23(3), 665–685 (1993)

    Article  Google Scholar 

  51. Stavroulakis, P.: Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications. Springer (2004)

  52. Fattahi, H., Shojaee, S., Farsangi, M.A.E., Mansouri, H.: Hybrid Monte Carlo simulation and ANFIS-subtractive clustering method for reliability analysis of the excavation damaged zone in underground spaces. Comput. Geotech. 54, 210–221 (2013)

    Article  Google Scholar 

  53. Fattahi, H., Shojaee, S., Farsangi, M.E.: Application of adaptive network-based fuzzy inference system for the assessment of damaged zone around underground spaces. Int. J. Optim. Civil. Eng. 3(4), 673–693 (2013)

    Google Scholar 

  54. Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy. Syst. 2(3), 267–278 (1994)

    Google Scholar 

  55. Avseth, P., Mukerji, T., Jørstad, A., Mavko, G., Veggeland, T: Seismic reservoir mapping from 3-D AVO in a North Sea turbidite system. Geophysics 66(4), 1157–1176 (2001)

    Article  Google Scholar 

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

    Article  Google Scholar 

  57. Karimpouli, S., Hassani, H., Khoshdel, H., Malehmir, A., Nabi-Bidhendi, M.: 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, 93–130 (2014)

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

    Article  Google Scholar 

  59. Buland, A., Kolbjørnsen, O., Hauge, R., Skjæveland, Ø., Duffaut, K.: Bayesian lithology and fluid prediction from seismic prestack data. Geophysics 73(3), C13–C21 (2008)

    Article  Google Scholar 

  60. Aki, K., Richards, P.G.: Quantitative seismology, vol. 1. W H Freeman & Co (2002)

  61. Tiwary, D.K., Bayuk, I.O., Vikhorev, A., Ammerman, M., Chesnokov, E.M.: Comparison of seismic upscaling methods (2007)

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

    Article  Google Scholar 

  63. Tiwary, D.K., Bayuk, I.O., Vikhorev, A.A., Chesnokov, E.M.: Comparison of seismic upscaling methods: from sonic to seismic. Geophysics 74(2), WA3-WA14 (2009)

    Article  Google Scholar 

  64. Mavko, G., Mukerji, T., Dvorkin, J.: The rock physics handbook: tools for seismic analysis of porous media. Cambridge University Press (2009)

  65. Dou, Q., Sun, Y., Sullivan, C.: Rock-physics-based carbonate pore type characterization and reservoir permeability heterogeneity evaluation, Upper San Andres reservoir, Permian Basin, west Texas. J. Appl. Geophys. 74(1), 8–18 (2011)

    Article  Google Scholar 

  66. Sheriff, R.E., Geldart, L.P.: Exploration seismology, 2nd edn. Cambridge University Press (1995)

  67. Jayalakshmi, T., Santhakumaran, A.: Statistical normalization and back propagation for classification. Int. J. Comput. Theory. Eng. 3(1), 1793–8201 (2011)

    Google Scholar 

  68. Gholami, R., Moradzadeh, A., Maleki, S., Amiri, S., Hanachi, J.: Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs. J. Pet. Sci. Eng. 122, 643–656 (2014)

    Article  Google Scholar 

  69. Chopra, S., Mitra, R., Kumar, V.: Reduction of fuzzy rules and membership functions and its application to fuzzy PI and PD type controllers. Int. J. Control. Autom. Syst. 4(4), 438 (2006)

    Google Scholar 

  70. Ming-bao, P., Xin-ping, Z.: Traffic flow prediction of chaos time series by using subtractive clustering for fuzzy neural network modeling. In: Second International Symposium on Intelligent Information Technology Application, 2008. IITA’08, pp. 23–27. IEEE (2008)

  71. Singh, V., Singh, D., Singh, T.: Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int. J. Rock. Mech. Min. Sci. 38(2), 269–284 (2001)

    Article  Google Scholar 

  72. Rabbani, E., Sharif, F., Salooki, M.K., Moradzadeh, A.: Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties. Int. J. Rock. Mech. Min. Sci. 56, 100–111 (2012)

    Google Scholar 

  73. Mohamad, E.T., Armaghani, D.J., Momeni, E., Abad, S.V.A.N.K.: Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull. Eng. Geology. Envir., 1–13 (2014)

  74. Barnes, A.E.: Theory of 2-D complex seismic trace analysis. Geophysics 61(1), 264–272 (1996)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mining Engineering, Arak University of Technology, Arak, Iran

    Hadi Fattahi

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

    Sadegh Karimpouli

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  1. Hadi Fattahi
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Fattahi, H., Karimpouli, S. Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods. Comput Geosci 20, 1075–1094 (2016). https://doi.org/10.1007/s10596-016-9577-0

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