انجمن ملی ژئوفیزیک ایران مجله ژئوفیزیک ایران 2008-0336 20 3 2026 07 23 3D modeling of electro-facies using a geostatistical algorithm 3D modeling of electro-facies using a geostatistical algorithm 129 151 225819 10.30499/ijg.2025.508999.1678 FA Reda Al Hasan M.Sc., Faculty of Chemical, Petroleum and Gas Engineering, Department of Petroleum Engineering, Semnan university, Semnan, Iran Mohammad Hossein Saberi Associate Professor, Faculty of Chemical, Petroleum and Gas Engineering, Department of Petroleum Engineering, Semnan University, Semnan, Iran 0000-0001-5248-3663 Mohammad Ali Riahi Professor, Institute of Geophysics, University of Tehran, Tehran, Iran 0000-0002-3827-4467 Journal Article 2025 03 01 <span>Facies analysis is a critical component of reservoir characterization studies. This study employs several clustering methods to generate reservoir electro-facies (EFs) from well logs. The clustering results were then distributed using geostatistical algorithms to create a reservoir facies model within a geometrical structure interpreted from seismic data. Well logs were classified into facies using multi-resolution graph-based clustering (MRGC) and self-organizing map (SOM) methods. Once distributed spatially with geostatistical techniques, the log-derived facies supported the conditional distribution of petrophysical properties by facies. A geostatistical approach, specifically sequential indicator simulation (SIS), was used to integrate well logs and interpreted seismic data, resulting in an accurate 3D facies model. This model was generated for the depth interval spanning the Frontier (Second Wall Creek) to the Crow Mountain horizons in the Teapot Dome. The 3D facies model aids in developing the field plan and identifying potential new well locations for drilling.</span> <span>Facies analysis is a critical component of reservoir characterization studies. This study employs several clustering methods to generate reservoir electro-facies (EFs) from well logs. The clustering results were then distributed using geostatistical algorithms to create a reservoir facies model within a geometrical structure interpreted from seismic data. Well logs were classified into facies using multi-resolution graph-based clustering (MRGC) and self-organizing map (SOM) methods. Once distributed spatially with geostatistical techniques, the log-derived facies supported the conditional distribution of petrophysical properties by facies. A geostatistical approach, specifically sequential indicator simulation (SIS), was used to integrate well logs and interpreted seismic data, resulting in an accurate 3D facies model. This model was generated for the depth interval spanning the Frontier (Second Wall Creek) to the Crow Mountain horizons in the Teapot Dome. The 3D facies model aids in developing the field plan and identifying potential new well locations for drilling.</span> https://www.ijgeophysics.ir/article_225819_655d0a6e3c2ded98e723ec6bc2b8ea6e.pdf