ترکیب مدل‌های شبکه شکستگی گسسته مثلثی ورونی (DFN) با تحلیل ابعاد فراکتالی توالی‌های پیچیده برای پیش‌بینی تخلخل و نفوذپذیری

نوع مقاله : مقاله پژوهشی

نویسنده

دانشکده مهندسی معدن، دانشکدگان فنی، دانشگاه تهران، تهران، ایران

چکیده

Optimizing reservoir performance in fractured reservoirs relies heavily on understanding and harnessing fracture connectivity at the reservoir scale. Voroni triangulation Discrete Fracture Network (DFN) models offer a unique depiction of fractures and their connectivity compared to other methods. Petrophysical property modeling involves various algorithms, with DFN emerging as a novel mathematical approach. This study centers on a segment of Khangiran's hydrocarbon formations, analyzing reservoir porosity and permeability. Among the plethora of available methods, fractal geometry, particularly through the box counting method, proves apt for estimating these properties. By increasing the box size to explore point distribution in the background space, the method calculates fractal dimensions, aiding in porosity and permeability estimation. Applied in modeling, this technique presents a new ellipsoid-based prediction model, providing a comprehensive description of petrophysical properties in reservoir-prone areas. The results, aligned with geological features, mud loss data, and production outcomes, demonstrate remarkable compatibility with lower uncertainty, presenting a promising avenue for enhanced reservoir characterization and performance optimization. The three-dimensional block model estimations derived from the Integrated Discrete Fracture Network (DFN) algorithm with a fractal dimension of complex sequences distribution align with well test analysis and production data results. The iterative application and refinement of the DFN algorithm and fractal dimension modeling process hold potential for further enhancement across the Khangiran reservoir or other hydrocarbon fields. The findings indicate that well 11 is optimally configured and likely exhibits superior performance in terms of hydrocarbon production within the reservoir.

کلیدواژه‌ها

عنوان مقاله [English]

Combining Voronoi Triangulation Discrete Fracture Network (DFN) Models with Fractal Dimension Analysis of Complex Sequences for Predicting Porosity and Permeability

نویسنده [English]

  • Hamid Sarkheil
School of Mining,, College of Engineering, University of Tehran, Tehran, Iran
چکیده [English]

Optimizing reservoir performance in fractured reservoirs relies heavily on understanding and harnessing fracture connectivity at the reservoir scale. Voroni triangulation Discrete Fracture Network (DFN) models offer a unique depiction of fractures and their connectivity compared to other methods. Petrophysical property modeling involves various algorithms, with DFN emerging as a novel mathematical approach. This study centers on a segment of Khangiran's hydrocarbon formations, analyzing reservoir porosity and permeability. Among the plethora of available methods, fractal geometry, particularly through the box counting method, proves apt for estimating these properties. By increasing the box size to explore point distribution in the background space, the method calculates fractal dimensions, aiding in porosity and permeability estimation. Applied in modeling, this technique presents a new ellipsoid-based prediction model, providing a comprehensive description of petrophysical properties in reservoir-prone areas. The results, aligned with geological features, mud loss data, and production outcomes, demonstrate remarkable compatibility with lower uncertainty, presenting a promising avenue for enhanced reservoir characterization and performance optimization. The three-dimensional block model estimations derived from the Integrated Discrete Fracture Network (DFN) algorithm with a fractal dimension of complex sequences distribution align with well test analysis and production data results. The iterative application and refinement of the DFN algorithm and fractal dimension modeling process hold potential for further enhancement across the Khangiran reservoir or other hydrocarbon fields. The findings indicate that well 11 is optimally configured and likely exhibits superior performance in terms of hydrocarbon production within the reservoir.

کلیدواژه‌ها [English]

  • Petrophysical Properties
  • DFN Models
  • Complex Sequences
  • Shorijeh
  • Khangiran

مراجع

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[12] Sarkheil H., Hassani H., and Alinia F., (2009a).The fracture network modeling in naturally fractured reservoirs using artificial neural network based on image loges and core measurements”, Australian J. of Basic and Applied Sciences, 3(4), pp. 3297–3306.
[13] Sarkheil H., Hassani H., Alinia F., Enayati A. A., and Motamedi H.,  (2009b). A forecasting system of reservoir fractures based on artificial neural network and borehole images information-exemplified by reservoir fractures in Tabnak field, Fars, Iran”, 9th International Multidisciplinary Scientific Geo-Conference, Vol. 1, pp. 563–570
[14] Sarkheil H., Hassani H., Alinia F., Enayati A. A., and Nikandish A. A., (2010). Fracture analysis in Tabnak hydrocarbon field of Iran by using fractal geometery and multi-fractal analysis”, J. Arab Geosci, Springer, Berlin, pp. 1-12.
[15] Sarkheil H., Hassani H. and Alinia F.,  (2013). Fractures Distribution Modeling Using Fractal and Multi-Fractal–Neural Network Analysis in Tabnak Hydrocarbon Field, Fars, Iran”, J. Arab Geosci, Springer, Berlin, 6: pp.945–956.
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[17] Sarkheil H., and Talebi A.,  (2014). Petrophysical properties modeling using of Integrated DFN algorithm with influence radius of permeability and porosity”, Second EAGE Integrated Reservoir Modeling Conference, (European Association of Geoscientists & Engineers), Dubai, UAE, 16 - 19 November, pp. 25-30.
[18] Sarkheil1, H., Hassani, H., (2021). Fractures Reservoir Distribution Modeling using Kriging-Geostatistical Patterns Recognition Analysis,  European Association of Geoscientists & Engineers, 82nd EAGE Annual Conference & Exhibition, Oct 2021, Volume 2021, p.1 – 5, https://doi.org/10.3997/2214-4609.202112840
[19] Sarkheil, H., Hassani, H. & Alinia, (2021). F. Fractured reservoir distribution characterization using folding mechanism analysis and patterns recognition in the Tabnak hydrocarbon reservoir anticline. J Petrol Explor Prod Technol 11, 2425–2433, https://doi.org/10.1007/s13202-021-01225-y
نشریه علمی ژئومکانیک نفت
دوره 6، شماره 3
شماره ویژه انگلیسی
آبان 1402
صفحه 74-81

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