A general approach for porosity estimation using artificial neural network method: a case study from Kansas gas field
- 222 Downloads
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.
Keywordsporosity estimation artificial neural network well log data Kansas gas field
Unable to display preview. Download preview PDF.
- Al-Qahtani F.A., 2000. Porosity Distribution Prediction Using Artificial Neural Networks. MSc Thesis, Morgantown Virginia University, West Virginia.Google Scholar
- 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
- 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
- Latta F.B., 1944. Geology and Ground-Water Resources of Finney and Gray Counties, Kansas. Kansas Geological Survey Bulletin 55 (http://www.kgs.ku.edu/General/Geology/Finney/index.html).Google Scholar
- 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
- 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
- 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
- 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
- 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 (http://www.rocksolidimages.com/pdf/neural_network.pdf).Google Scholar
- Wackerly D. and Scheaffer W., 2008. Mathematical Statistics with Applications. 7th Edition. Thomson Brooks/Cole, Duxbury, MA.Google Scholar