The use of fractal geostatistics and artificial neural networks for carbonate reservoir characterization

Date

2000-11-01

Author

Yeten, B
Gumrah, F

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In this study, a carbonate oil reservoir located in the southeast part of Turkey was characterized by the use of kriging and the fractal geometry. The three-dimensional porosity and permeability distributions were generated by both aforementioned methods by using the wireline porosity logs and core plug permeability measurements taken from six wells of the field. Since classical regression (lognormal or polynomial) and geostatistical techniques (cross variograms) fail to estimate permeability from wireline log-porosity data, the use of artificial neural networks (ANNs) is proposed in this study to generate permeability data at uncored intervals of porosity logs. For both of the methods, kriging and fractal techniques, the validation of the estimated/simulated data with known wellbore data resulted with acceptable agreements, especially for porosity. Also the comparison of both methods at unsampled locations show better agreements for porosity than permeability.

Subject Keywords

Artificial neural networks, Carbonate reservoir characterization, Geostatistics, Fractals

URI

https://hdl.handle.net/11511/65695

Journal

TRANSPORT IN POROUS MEDIA

DOI

https://doi.org/10.1023/a:1006725709303

Collections

Department of Petroleum and Natural Gas Engineering, Article

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Citation Formats

B. Yeten and F. Gumrah, “The use of fractal geostatistics and artificial neural networks for carbonate reservoir characterization,” TRANSPORT IN POROUS MEDIA, pp. 173–195, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65695.