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

Studia Geophysica et Geodaetica - Tập 60 Số 1 - Trang 130-140 - 2016
Sagar Singh, Ali İsmet Kanlı1, Selçuk Sevgen2
1Department of Geophysical Engineering, Faculty of Engineering, Istanbul University, Avcilar Campus, Istanbul, Turkey
2Department of Computer Engineering, Faculty of Engineering, Istanbul University, Avcilar Campus, Istanbul, Turkey

Tóm tắt

Từ khóa


Tài liệu tham khảo

Al-Qahtani F.A., 2000. Porosity Distribution Prediction Using Artificial Neural Networks. MSc Thesis, Morgantown Virginia University, West Virginia.

Aminian K. and Ameri S., 2005. Application of artificial neural networks for reservoir characterization with limited data. J. Petrol. Sci. Eng., 49, 212–222.

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.

Baan M. and Jutten C., 2000. Neural networks in geophysical applications. Geophysics, 65, 1032–1047.

Bhatt A. and Helle H.B., 2002. Committee neural networks for porosity and permeability prediction from well logs. Geophys. Prospect., 50, 645–660.

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.

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.

Hearst J.R., Nelson P.H. and Paillet F.L., 2000. Well Logging for Physical Properties. John Wiley and Sons Ltd., New York.

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.

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.

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.

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.

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).

Nikravesh M. and Aminzadeh F., 2001. Past, present and future intelligent reservoir characterization trends. J. Petrol. Sci. Eng., 31, 67–79.

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.

Ouenes A., 2000. Practical application of fuzzy logic and neural networks to fractured reservoir characterization. Comput. Geosci., 26, 953–962.

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.

Russell B., Hampson D., Schuelke J. and Quirein J., 1997. Multiattribute seismic analysis. Leading Edge, 16, 1439–1443.

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.

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.

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.

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).

Wackerly D. and Scheaffer W., 2008. Mathematical Statistics with Applications. 7th Edition. Thomson Brooks/Cole, Duxbury, MA.

Wong P.M. and Nikravesh M., 2001. Introduction: field applications of intelligent computing techniques. J. Petrol. Geol., 24, 381–387.

Wyllie M.R.J., Gregory A.R. and Gardner L.W., 1956. Elastic wave velocities in heterogeneous and porous media. Geophysics, 21, 41–70.