Kadkhodaie, Ali, Hosseinzadeh, Sirous, Mosaddegh, Hossein, Kadkhodaie, Rahim. (1398). Pore throat size characterization of carbonate reservoirs by integrating core data, well logs and seismic attributes. , 9(2), 395-410. doi: 10.22059/geope.2019.269872.648430
Ali Kadkhodaie; Sirous Hosseinzadeh; Hossein Mosaddegh; Rahim Kadkhodaie. "Pore throat size characterization of carbonate reservoirs by integrating core data, well logs and seismic attributes". , 9, 2, 1398, 395-410. doi: 10.22059/geope.2019.269872.648430
Kadkhodaie, Ali, Hosseinzadeh, Sirous, Mosaddegh, Hossein, Kadkhodaie, Rahim. (1398). 'Pore throat size characterization of carbonate reservoirs by integrating core data, well logs and seismic attributes', , 9(2), pp. 395-410. doi: 10.22059/geope.2019.269872.648430
Kadkhodaie, Ali, Hosseinzadeh, Sirous, Mosaddegh, Hossein, Kadkhodaie, Rahim. Pore throat size characterization of carbonate reservoirs by integrating core data, well logs and seismic attributes. , 1398; 9(2): 395-410. doi: 10.22059/geope.2019.269872.648430

Pore throat size characterization of carbonate reservoirs by integrating core data, well logs and seismic attributes

Geopersia
مقاله 13، دوره 9، شماره 2 - شماره پیاپی 2228782، بهمن 2019، صفحه 395-410    اصل مقاله (2.47 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/geope.2019.269872.648430
نویسندگان
Ali Kadkhodaie1؛ Sirous Hosseinzadeh2؛ Hossein Mosaddegh* 2؛ Rahim Kadkhodaie1
1Department of geology, Faculty of Sciences, University of Tabriz, Tabriz, Iran
2Department of geology, Faculty of Sciences, Kharazmi University, Tehran, Iran
چکیده
Investigation of pore system properties of carbonate reservoirs has an important role in evaluating the reservoir quality and delineating high production intervals. The current study proposes a three-step approach for pore throat size characterization of these reservoirs, by integrating core data, well logs and 3D seismic volume. In this respect, first the pore throats size was calculated using Pittman and Winland models based upon routine core analysis data, and calibrating the results with the laboratory-derived capillary pressure curves. In the second step, the pore throat size as a continuous log was calculated using petrophysical data for each studied well. Finally, the calculated pore throat size log was tied to 3D seismic data at well locations. The results show that seismic attributes including acoustic impedance, amplitude envelope, filter 15/20-25/30 and derivative instantaneous amplitude are the best predictor set for converting the 3D seismic volume into a pore size cube by a probabilistic neural network mode. The methodology illustrated in this study, was employed on Ilam carbonate reservoir in one of the southwestern oilfields of Iran.
The findings demonstrate that seismic data in combination with core and well log data could be considered as an effective tool for spatial modeling and characterization.
کلیدواژه‌ها
Pore throat size؛ Artificial Neural Network؛ seismic attributes؛ seismic inversion؛ carbonate reservoirs
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آمار
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