Reservoir facies and porosity modeling using seismic data and well logs by geostatistical simulation in an oil field
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Majid Bagheri [1] ; Mohammadreza Ebadi [2]
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[1] University of Tehran
University of Tehran
Irán
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[2] Petroleum University of Technology
Petroleum University of Technology
Irán
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- Localización: Carbonates and Evaporites, ISSN 0891-2556, Vol. 35, Nº. 3, 2020
- Idioma: inglés
- Enlaces
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Resumen
Reservoir characterization of petroleum reservoirs can be claimed as one of the most important parts of reservoir management for optimized production and future developments. Through reservoir evaluation, geological zoning for a better comprehension of subsurface structure is needed. For developing a model, porosity plays a vital role; there are two common methods for obtaining this parameter, core samples and well logging. However, the results of these methods are in well scale which cannot be used through field scale modeling. A solution can be the combining seismic field data well log data, which makes it possible to estimate the reservoir properties in field scale. In this study, multi-attribute analyses were applied based on multilayer perceptron to determine the reservoir facies alteration and heterogeneity in the Ghar reservoir of the Hendijan oil field located in the Persian Gulf. Facies modeling was done through the sequential indicator simulation (SIS) algorithm which coupled with the possible trend and indicator kriging (IK) as geostatistical methods. Within the comparison of these two generated models with core facies, the obtained accuracy of SIS algorithm coupled with the possible trend and indicator kriging are 94% and 72%, respectively. Porosity distribution was also done by the sequential Gaussian simulation (SGS) algorithm which resulted the average porosity of 18% in Ghar formation. The SIS method results are compatible with the porosity distribution model obtained from the SGS simulation. The final results prove the robustness of the applied methods for facies and porosity modeling.
