Identification of the bound and free fluid pore types in an Iranian carbonate reservoir through the integration of well logs, rock physics modeling, and 3D seismic data.
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| Title: | Identification of the bound and free fluid pore types in an Iranian carbonate reservoir through the integration of well logs, rock physics modeling, and 3D seismic data. |
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| Authors: | Mirshadi, Ahadollah1 (AUTHOR) mirshadi@aut.ac.ir, Javaherian, Abdolrahim1 (AUTHOR) javaherian@aut.ac.ir, Khoshdel, Hossein1 (AUTHOR) khoshdel.h@gmail.com, Saberi, Mohammad Reza2 (AUTHOR) saberi.rp@gmail.com, Kadkhodaie, Ali3 (AUTHOR) kadkhodaie_ali@tabriz.ac.ir |
| Source: | Journal of Petroleum Exploration & Production Technology. Jun2025, Vol. 15 Issue 6, p1-19. 19p. |
| Abstract: | Characterizing pore types in carbonate rocks is essential for understanding their reservoir properties, such as water saturation, porosity, and permeability, as well as their reservoir quality and dynamic behavior. The aim and objective of this study are primarily to identify the pore types and then, as an innovative approach, to specify whether the fluid within the pores in the reservoir is producible or remains immobile. To achieve this goal, bound and free fluid pore types are defined using nuclear magnetic resonance logs and post-stack 3D seismic data from an Iranian oil reservoir for the Main Ilam carbonate Formation. Initially, the fullset of logs for two wells (A and B) were used to determine porosity, lithology, and fluid content, followed by the nuclear magnetic resonance log to identify macro, meso, micro, and clay pore types. Subsequently, in well A, the results were validated through pore-size distribution analysis of the available core samples. Afterward, a rock physics model for carbonated rocks was used to estimate compressional velocity and density based on petrophysical evaluation and pore-type calculation. The correlation coefficients for compressional velocity in wells A and B of the Main Ilam Formation were 92% and 80%, respectively. The strong correlation among measured and modeled values verified that nuclear magnetic resonance data effectively estimates pore-type variations. Furthermore, bounded fluid was calculated based on the clay and micro pores, while free fluid was calculated based on the meso and macro pores in both wells. Finally, this information was mapped into the 3D seismic cube using inversion and multi-attribute regression analysis.Highlights: Identifying pore types is crucial for assessing reservoir characteristics, supporting the development of static and dynamic models, and optimizing the number of wells to drill. This study assesses fluid productivity from various pore types in the Main Illam carbonate Formation of an Iranian oil field using petrophysical evaluation, the NMR log, rock physics modeling, and post-stack 3D seismic data. The petrophysical evaluation yields data on fluid content, porosity, and lithology, while the NMR log identifies pore types, which are used as input for the Xu and Payne (2009) model to predict density and P-wave velocity. Calculations for pore types were made, with bound fluid types defined as the sum of clay and micro-pore types, and free fluid types consisting of meso and macro-pore types. The identification of pore types within the 3D seismic cube of the Main Ilam Formation was achieved through inversion and multi-attribute regression analysis. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Characterizing pore types in carbonate rocks is essential for understanding their reservoir properties, such as water saturation, porosity, and permeability, as well as their reservoir quality and dynamic behavior. The aim and objective of this study are primarily to identify the pore types and then, as an innovative approach, to specify whether the fluid within the pores in the reservoir is producible or remains immobile. To achieve this goal, bound and free fluid pore types are defined using nuclear magnetic resonance logs and post-stack 3D seismic data from an Iranian oil reservoir for the Main Ilam carbonate Formation. Initially, the fullset of logs for two wells (A and B) were used to determine porosity, lithology, and fluid content, followed by the nuclear magnetic resonance log to identify macro, meso, micro, and clay pore types. Subsequently, in well A, the results were validated through pore-size distribution analysis of the available core samples. Afterward, a rock physics model for carbonated rocks was used to estimate compressional velocity and density based on petrophysical evaluation and pore-type calculation. The correlation coefficients for compressional velocity in wells A and B of the Main Ilam Formation were 92% and 80%, respectively. The strong correlation among measured and modeled values verified that nuclear magnetic resonance data effectively estimates pore-type variations. Furthermore, bounded fluid was calculated based on the clay and micro pores, while free fluid was calculated based on the meso and macro pores in both wells. Finally, this information was mapped into the 3D seismic cube using inversion and multi-attribute regression analysis.Highlights: Identifying pore types is crucial for assessing reservoir characteristics, supporting the development of static and dynamic models, and optimizing the number of wells to drill. This study assesses fluid productivity from various pore types in the Main Illam carbonate Formation of an Iranian oil field using petrophysical evaluation, the NMR log, rock physics modeling, and post-stack 3D seismic data. The petrophysical evaluation yields data on fluid content, porosity, and lithology, while the NMR log identifies pore types, which are used as input for the Xu and Payne (2009) model to predict density and P-wave velocity. Calculations for pore types were made, with bound fluid types defined as the sum of clay and micro-pore types, and free fluid types consisting of meso and macro-pore types. The identification of pore types within the 3D seismic cube of the Main Ilam Formation was achieved through inversion and multi-attribute regression analysis. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 21900558 |
| DOI: | 10.1007/s13202-025-02004-9 |
