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Integrated Petrophysical Modeling for a Strongly Heterogeneous and Fractured Reservoir, Sarvak Formation, SW Iran

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Abstract

Introducing and applying an appropriate strategy for reservoir modeling in strongly heterogeneous and fractured reservoirs is a controversial issue in reservoir engineering. Various integration approaches have been introduced to combine different sources of information and model building techniques to handle heterogeneity in geological complex reservoir. However, most of these integration approaches in several studies fail on modeling strongly fractured limestone reservoir rocks of the Zagros belt in southwest Iran. In this study, we introduced a new strategy for appropriate modeling of a production formation fractured rock. Firstly, different rock types in the study area were identified based on well log data. Then, the Sarvak Formation was divided into nine zones, and the thinner subzones were used for further fine modeling procedure. These subzones were separated based on different fracture types and fracture distribution in each zone. This strategy provided sophisticated distribution of petrophysical parameters throughout the grids of the model, and therefore, it can handle strong heterogeneity of the complex reservoir. Afterward, petrophysical parameters were used to produce an up-scaled 3D gridded petrophysical model. Subsequently, maps of petrophysical properties were derived for each zone of the Sarvak Formation. Evidences achieved in this study indicates Sarvak Formation zone 2 as the target production zone with better performance of reservoir rock and the southwestern part of the field as area of maximum porosity.

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References

  • Abdideh, M., & Bargahi, D. (2012). Designing a 3D model for prediction the top of formation in oil fields using geostatistical methods. Geocartogrpahiy International Journal, 27, 569–579.

    Article  Google Scholar 

  • Aguilera, R. (2004). Integration of geology, petrophysics, and reservoir engineering for characterization of carbonate reservoirs through Pickett plots. AAPG Bulletin, 88(4), 433–446.

    Article  Google Scholar 

  • Beiranvand, B., & Kamali, M. R. (2004). Petrophysical evaluation and determination of rock types in a carbonate reservoir in SW Iran with interpretation of petrography and geophysical well logs. Iranian International Journal of Science, 5(2), 203–221.

    Google Scholar 

  • Bordenave, M. L. (2002). The middle Cretaceous to early Miocene petroleum system in the Zagros domain of Iran, and its prospect evaluation. In: AAPG annual meeting, Houston, TX.

  • Bordenave, M. L., & Burwood, R. (1990). Source rock distribution and maturation in the belt, provenance of the Asmari and Sarvak reservoirs oil accumulations. Organic Geochemistry, 16, 369–387.

    Article  Google Scholar 

  • Bordenave, M. L., & Burwood, R. (1995). The albian Kazhdumi formation of the Dezful embayment, Iran: One of the most efficient petroleum generating systems. The petroleum source rocks series in Earth Sciences. Heidelberg: Springer.

    Google Scholar 

  • Bordenave, M. L., & Huc, A. Y. (1993). The cretaceous source rocks in the Zagros foothills of Iran: An example of a large size intracratonic basin (pp. 727–753). Caracas, Venezuela: International Congress and Exhibition.

    Google Scholar 

  • Burchette, T. P. (2012). Carbonate rocks and petroleum reservoirs: A geological perspective from the industry. Geological Society of London., 370, 17–37.

    Article  Google Scholar 

  • Caers, J. (2001). Geostatistical reservoir modeling using statistical pattern recognition. Journal of Petroleum Science and Engineering, 29(3–4), 177–188.

    Article  Google Scholar 

  • Cao, H. (2002). Development of techniques for general purpose simulators. PhD dissertation, Stanford University, Stanford, CA.

  • Daneh Dezfuli, A., & Bahrainian, S. S. (2014). A grid generation algorithm for reservoir domains with discrete fracture networks. International Journal of Oil, Gas and Coal Technology, 8(4), 417–431.

    Article  Google Scholar 

  • Dang, T. Q. C., Chen, Z., Nguyen, N. T. B., & Phung, T. H. (2015). An integrated geology and reservoir engineering approach for modeling of a giant fractured basement reservoir. International Journal of Oil, Gas and Coal Technology, 10(1), 39–59.

    Article  Google Scholar 

  • Demyanov, V., Backhous, L., & Christi, M. (2015). Geological feature selection in reservoir modeling and history matching with Multiple Kernel Learning. Computers & Geosciences, 85, 16–25.

    Article  Google Scholar 

  • Fanchi, J. R. (2006). Principles of applied reservoir simulation. Burlington: Gulf Publishing, Elsevier.

    Google Scholar 

  • Gong, B., Karimi-Fard, M., & Durlofsky, L. J. (2006). An upscaling procedure for constructing generalized dual-porosity/dual permeability model from discrete fracture characterizations. In Proceeding of annual technical conference and exhibition, Texas.

  • Haining, R. (2003). Spatial data analysis: Theory and practice. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Holden, L., Mostad, P., Nielsen, B. F., Gjered, J., Townsend, C., & Ottesen, S. (2003). Stochastic structural modeling. Journal of Mathematical Geology, 35(8), 899–914.

    Article  Google Scholar 

  • Jafari, A., & Babadagli, T. (2012). Estimation of equivalent fracture network permeability using fractal and statistical network properties. Journal of Petroleum Science and Engineering, 92, 110–123.

    Article  Google Scholar 

  • Jones, R. R., McCaffrey, K. J. W., Clegg, P., Wilson, R. W., Holliman, N. S., Holdworth, R. E., et al. (2009). Integration of regional to outcrop digital data: 3D visualization of multi-scale geological models. Computers & Geosciences, 35(1), 4–18.

    Article  Google Scholar 

  • Kalantari-Dahaghi, A., & Mohaghegh, S. D. (2011). A new practical approach in modeling and simulation of shale gas reservoirs: Application to New Albany Shale. International Journal of Oil, Gas and Coal Technology, 4(2), 104–133.

    Article  Google Scholar 

  • Kamali, M. R., Omidvar, A., & Kazemzadeh, E. (2013). 3D geostatistical modeling and uncertainty analysis in a carbonate reservoir, SW Iran. Journal of Geological Research. doi:10.1155/2013/687947.

    Google Scholar 

  • Karimi-Fard, M., Durlofsky, L. J., & Aziz, K. (2004). An efficient discrete fracture model applicable for general-purpose reservoir simulators. Society of Petroleum Engineers Journal, 9(2), 227–236.

    Google Scholar 

  • Karimi-Fard, M., Gong, B., & Durlofsky, L. J. (2006). Generation of coarse-scale continuum flow models from detailed fracture characterizations. Water Resource Research, 42(10), 1–13.

    Article  Google Scholar 

  • Kaufmann, O., & Martin, T. (2008). 3D geological modeling from boreholes, cross section and geological maps, application over former natural gas storages in coal mines. Computers & Geosciences, 34, 278–290.

    Article  Google Scholar 

  • Koike, K., Kubo, T., Liu, C., Masoud, A., Amano, K., Kurihara, A., et al. (2015). 3D geostatistical modeling of fracture system in a granitic massif to characterize hydraulic properties and fracture distribution. Tectnophysiscs, 660, 1–16.

    Article  Google Scholar 

  • Lelliott, M. R., Cave, M. R., & Walthall, G. P. (2009). A structured approach to the measurement of uncertainty in 3D geological model. Journal of Engineering Geology and Hydrogeology, 42(1), 95–105.

    Article  Google Scholar 

  • Ma, Y. Z., & La Pointe, P. R. (2011). Uncertainty analysis and reservoir modeling. Tulsa: AAPG Publishing.

    Google Scholar 

  • Manzocchi, M., & Walsh, J. (2002). The SAIGUP project—Sensitivity analysis of the impact of geological uncertainties on production forecasting in clastic hydrocarbon reservoirs. In 64th EAGE conference and exhibition Florence, Italy.

  • Maschio, C., Vidal, A. C., & Schiozer, D. J. (2008). A framework to integrate history matching and geostatistical modeling using genetic algorithm and direct search methods. Journal of Petroleum Science and Engineering, 63(1), 34–42.

    Article  Google Scholar 

  • Mohaghegh, S. D., Gruic, O., Zargari, S., Kalantari-Dahaghi, A., & Bromhal, G. S. (2012). Top-down, intelligent reservoir modeling of oil and gas producing shale reservoirs: Case studies. International Journal of Oil, Gas and Coal Technology, 5(1), 3–28.

    Article  Google Scholar 

  • Mohaghegh, S. D., Modavi, C. A., Hafez, H. H., Haajizadeh, M., Kenawy, M. M., & Guruswamy, S. (2009). Development of surrogate reservoir model (SRM) for fast track analysis of a complex reservoir. International Journal of Oil, Gas and Coal Technology, 2(1), 2–23.

    Article  Google Scholar 

  • Moradi, S., Moghani-Rahimi, A., Zargar, G., & Kakavandi, M. (2015). Investigation of cementation exponent (m) in heterogeneous carbonate reservoirs via integration of thin section analysis and well logging. International Journal of Oil, Gas and Coal Technology, 9(4), 379–393.

    Article  Google Scholar 

  • Nazari Ostad, M., Asgharia, O., Emery, X., Azizzadeh, M., & Khoshbakht, F. (2016). Fracture network modeling using petrophysical data, an approach based on geostatistical concepts. Journal of Natural Gas Science and Engineering, 31, 758–768.

    Article  Google Scholar 

  • Nikravesh, M. (2007). Computational intelligence for geosciences and oil exploration. Forging New Frontiers, Fuzzy Pioneers, 66, 267–332.

    Article  Google Scholar 

  • Olowokere, M. T. (2010). Geostatistical modeling of interval velocity to quantifying hydrocarbon resource in multi-layer reservoir from TMB field, Niger delta. International Journal of Physical Sciences, 5(12), 1897–1907.

    Google Scholar 

  • Palermo, D., Aigner, T., Seyfang, B., & Nardon, S. (2012). Reservoir properties and petrophysical modeling of carbonate sand bodies: Outcrop analogue study in an epicontinental basin. London, Special Publications: Geological Society.

    Google Scholar 

  • Prévost, M., Lepage, F., Durlofsky, L. J., & Mallet, J. L. (2005). Unstructured 3D gridding and upscaling for coarse modeling of geometrically complex reservoirs. Petroleum Geoscience, 11(4), 339–345.

    Article  Google Scholar 

  • Pulido, H., Samaniego, F., Galicia-Muñoz, G., Rivera, J., & Vélez, C. (2007). Petrophysical characterization of carbonate naturally fractured reservoirs for use in dual porosity simulators proceedings. In 32nd conference on geothermal reservoir engineering. SGP-TR-183. Stanford, CA: Stanford University.

  • Rühaak, W., Bär, K., & Sass, I. (2014). Combining numerical modeling with geostatistical interpolation for an improved reservoir exploration. Energy Procedia, 59, 315–322.

    Article  Google Scholar 

  • Sacchi, Q., Borello, E. S., Weltje, G. J., & Dalman, R. (2016). Increasing the predictive power of geostatistical reservoir models by integration of geological constraints from stratigraphic forward modeling. Marine and Petroleum Geology, 69, 112–126.

    Article  Google Scholar 

  • Samadi, H. R., & Salehi, M. H. (2013). Static 3D modeling of hydrocarbon reservoir with the help of RMS case of studies: The South East anticline of Khuzestan Iran. Journal of Basic and Applied Science Research, 3(2), 473–477.

    Google Scholar 

  • Samimi, A. K., & Karimi, G. (2014). Sensitivity and uncertainty analysis of original oil in place in carbonate reservoir modeling, a case study. Petroleum and Coal, 56(3), 332–338.

    Google Scholar 

  • Soleimani, M. (2016). Seismic imaging by 3D partial CDS method in complex media. Journal of Petroleum Science and Engineering, 143, 54–64.

    Article  Google Scholar 

  • Soleimani, M., & Zarvani, A. S. (2009). Lithological and petrophysical evaluation of the caprock keybeds, Asmari Reservoir of Pazanan Oil Field, Zagros, Iran. Songklanakarin Journal of Science and Technology, 31(6), 655–660.

    Google Scholar 

  • Soliemani, M., & Jodeiri Shokri, B. (2015). 3D static reservoir modeling by geostatistical techniques used for reservoir characterization and data integration. Environmental Earth Science, 74, 1403–1414.

    Article  Google Scholar 

  • Wu, H., & Pollard, D. D. (2002). Imaging 3-D fracture networks around boreholes. American Association of Petroleum Geologist Bulletin, 86, 593–604.

    Google Scholar 

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Acknowledgments

The authors would like to thank four anonymous reviewers for their constructive comments in improving the manuscript.

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Authors and Affiliations

  1. Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran

    Mehrdad Soleimani

  2. Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran

    Behshad Jodeiri Shokri

  3. School of Geology, College of Science, University of Tehran, Tehran, Iran

    Mehrnoush Rafiei

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  1. Mehrdad Soleimani
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  2. Behshad Jodeiri Shokri
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Correspondence to Behshad Jodeiri Shokri.

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Soleimani, M., Jodeiri Shokri, B. & Rafiei, M. Integrated Petrophysical Modeling for a Strongly Heterogeneous and Fractured Reservoir, Sarvak Formation, SW Iran. Nat Resour Res 26, 75–88 (2017). https://doi.org/10.1007/s11053-016-9300-9

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