Studia Geophysica et Geodaetica

, Volume 60, Issue 1, pp 130–140 | Cite as

A general approach for porosity estimation using artificial neural network method: a case study from Kansas gas field

  • Sagar Singh
  • Ali Ismet Kanli
  • Selcuk Sevgen


This study aims to design a back-propagation artificial neural network (BP-ANN) to estimate the reliable porosity values from the well log data taken from Kansas gas field in the USA. In order to estimate the porosity, a neural network approach is applied, which uses as input sonic, density and resistivity log data, which are known to affect the porosity. This network easily sets up a relationship between the input data and the output parameters without having prior knowledge of petrophysical properties, such as porefluid type or matrix material type. The results obtained from the empirical relationship are compared with those from the neural network and a good correlation is observed. Thus, the ANN technique could be used to predict the porosity from other well log data.


porosity estimation artificial neural network well log data Kansas gas field 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Al-Qahtani F.A., 2000. Porosity Distribution Prediction Using Artificial Neural Networks. MSc Thesis, Morgantown Virginia University, West Virginia.Google Scholar
  2. Aminian K. and Ameri S., 2005. Application of artificial neural networks for reservoir characterization with limited data. J. Petrol. Sci. Eng., 49, 212–222.CrossRefGoogle Scholar
  3. Arabani M.S. and Bidhendi M., 2002. Porosity prediction from wireline logs using artificial neural networks: a case study in north-east of Iran. Iranian Int. J. Sci., 3, 221–233.Google Scholar
  4. Baan M. and Jutten C., 2000. Neural networks in geophysical applications. Geophysics, 65, 1032–1047.CrossRefGoogle Scholar
  5. Bhatt A. and Helle H.B., 2002. Committee neural networks for porosity and permeability prediction from well logs. Geophys. Prospect., 50, 645–660.CrossRefGoogle Scholar
  6. Dorrington K.P. and Link C.A., 2004. Genetic-algorithm/neural-network approach to seismic attribute selection for well-log prediction. Geophysics, 69, 212–221.CrossRefGoogle Scholar
  7. Gastaldi C., Biguenet J. and Pazzis L.D., 1997. Reservoir characterization from seismic attributes: an example from the Peciko field (Indonesia). Leading Edge, 16, 263–266.CrossRefGoogle Scholar
  8. Hearst J.R., Nelson P.H. and Paillet F.L., 2000. Well Logging for Physical Properties. John Wiley and Sons Ltd., New York.Google Scholar
  9. Helle H.B., Bhatt A. and Ursin B., 2001. Porosity and permeability prediction from wireline logs using artificial neural networks: a North Sea case study. Geophys. Prospect., 49, 431–444.CrossRefGoogle Scholar
  10. Hampson D.P., Schuelke J.S. and Quierin J.A., 2001. Use of multiattribute transforms to predict log properties from seismic data. Geophysics, 66, 220–236.CrossRefGoogle Scholar
  11. Iturrarán-Viveros U. and Parra J.O., 2014. Artificial neural networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data. J. Appl. Geophys., 107, 45–54.CrossRefGoogle Scholar
  12. Kaydani H., Mohebbi A. and Baghaie A., 2012. Neural fuzzy system development for the prediction of permeability from wireline data based on fuzzy clustering. Petrol. Sci. Technol., 30, 2036–2045.CrossRefGoogle Scholar
  13. Latta F.B., 1944. Geology and Ground-Water Resources of Finney and Gray Counties, Kansas. Kansas Geological Survey Bulletin 55 ( Scholar
  14. Nikravesh M. and Aminzadeh F., 2001. Past, present and future intelligent reservoir characterization trends. J. Petrol. Sci. Eng., 31, 67–79.CrossRefGoogle Scholar
  15. Nikravesh M., Aminzadeh F. and Zadeh L.A., 2003. Soft Computing and Intelligent Data Analysis in Oil Exploration. Developments in Petroleum Sciences 51. Elsevier, Amsterdam, The Netherlands.Google Scholar
  16. Ouenes A., 2000. Practical application of fuzzy logic and neural networks to fractured reservoir characterization. Comput. Geosci., 26, 953–962.CrossRefGoogle Scholar
  17. Pramanik A.G., Singh V., Vig R., Srivastava A.K. and Tiwary D.N., 2004. Estimation of effective porosity using geostatistics and multiattribute transforms: a case study. Geophysics, 69, 352–372.CrossRefGoogle Scholar
  18. Russell B., Hampson D., Schuelke J. and Quirein J., 1997. Multiattribute seismic analysis. Leading Edge, 16, 1439–1443.CrossRefGoogle Scholar
  19. Russell B.H. 2004. The Application of Multivariate Statistics and Neural Networks to the Prediction of Reservoir Parameters Using Seismic Attributes. PhD Thesis, University of Calgary, Calgary, Alberta, Canada.Google Scholar
  20. Serra O. 1984a. Formation density measurements (the gamma-gamma log or density log). In: Serra O. (Ed.), Fundamentals of well-log interpretation. Developments in Petroleum Sciences 15A. Elsevier, Amsterdam, The Netherlands, 195–204.Google Scholar
  21. Serra O. 1984b. The measurement of resistivity. In: Serra O. (Ed.), Fundamentals of well-log interpretation. Developments in Petroleum Sciences 15A. Elsevier, Amsterdam, The Netherlands, 51–76.Google Scholar
  22. Taner M., 1995. Neural networks and computation of neural network weights and biases by the generalized delta rule and back-propagation of errors. Rock Solid Images ( Scholar
  23. Wackerly D. and Scheaffer W., 2008. Mathematical Statistics with Applications. 7th Edition. Thomson Brooks/Cole, Duxbury, MA.Google Scholar
  24. Wong P.M. and Nikravesh M., 2001. Introduction: field applications of intelligent computing techniques. J. Petrol. Geol., 24, 381–387.CrossRefGoogle Scholar
  25. Wyllie M.R.J., Gregory A.R. and Gardner L.W., 1956. Elastic wave velocities in heterogeneous and porous media. Geophysics, 21, 41–70.CrossRefGoogle Scholar

Copyright information

© Institute of Geophysics of the ASCR, v.v.i 2015

Authors and Affiliations

  1. 1.Department of Earth SciencesIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Department of Geophysical Engineering, Faculty of EngineeringIstanbul University, Avcilar CampusIstanbulTurkey
  3. 3.Department of Computer Engineering, Faculty of EngineeringIstanbul University, Avcilar CampusIstanbulTurkey

Personalised recommendations