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

Yeten B., Gumrah F.

TRANSPORT IN POROUS MEDIA, vol.41, no.2, pp.173-195, 2000 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 2
  • Publication Date: 2000
  • Doi Number: 10.1023/a:1006725709303
  • Journal Name: TRANSPORT IN POROUS MEDIA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.173-195
  • Keywords: carbonate reservoir characterization, geostatistics, fractals, artificial neural networks, SAN-ANDRES FORMATION, OUTCROP ANALOG, PERMIAN BASIN
  • Middle East Technical University Affiliated: No

Abstract

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.