مجله ژئوفیزیک ایران

مجله ژئوفیزیک ایران

3D modeling of electro-facies using a geostatistical algorithm

مقالات آماده انتشار

نوع مقاله : مقاله پژوهشی‌

نویسندگان
Reda Al Hasan 1
Mohammad Hossein saberi 2
Mohammad Ali Riahi 3
1 M.Sc., Faculty of Chemical, Petroleum and Gas Engineering, Department of Petroleum Engineering, Semnan university, Semnan, Iran
2 Associate Professor, Faculty of Chemical, Petroleum and Gas Engineering, Department of Petroleum Engineering, Semnan University, Semnan, Iran
3 Professor, Institute of Geophysics, University of Tehran, Tehran, Iran
10.30499/ijg.2025.508999.1678
چکیده
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.
کلیدواژه‌ها
Sequential indicator simulation (SIS), electro-facies, lithofacies, MRGC, SOM

موضوعات

عنوان مقاله English

3D modeling of electro-facies using a geostatistical algorithm

نویسندگان English

Reda Al Hasan 1
Mohammad Hossein Saberi 2
Mohammad Ali Riahi 3
1 M.Sc., Faculty of Chemical, Petroleum and Gas Engineering, Department of Petroleum Engineering, Semnan university, Semnan, Iran
2 Associate Professor, Faculty of Chemical, Petroleum and Gas Engineering, Department of Petroleum Engineering, Semnan University, Semnan, Iran
3 Professor, Institute of Geophysics, University of Tehran, Tehran, Iran
چکیده English

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.

کلیدواژه‌ها English

Sequential indicator simulation (SIS), electro-facies, lithofacies, MRGC, SOM
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مجله ژئوفیزیک ایران

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