A new method in reservoir rock classification in carbonate and sandstone formations

Abstract This study aims to improve rock-type classification by analyzing core data based on the water–oil primary drainage capillary pressure method. A modified empirical equation is proposed using permeability, porosity, and irreducible water saturation to classify rock types based on water–oil pr...

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Veröffentlicht in:Journal of geophysics and engineering 2023-07, Vol.20 (4), p.883-900
Hauptverfasser: Omrani, Hashem, Hajipour, Mastaneh, Jamshidi, Saeid, Behnood, Mohammad
Format: Artikel
Sprache:eng
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Zusammenfassung:Abstract This study aims to improve rock-type classification by analyzing core data based on the water–oil primary drainage capillary pressure method. A modified empirical equation is proposed using permeability, porosity, and irreducible water saturation to classify rock types based on water–oil primary drainage capillary pressure. We used primary drainage capillary pressure data measured in carbonate and sandstone samples in the Ahvaz Asmari and Mansouri oilfields to evaluate the characterization number (Cn) method. This study consists of two main parts. First, the Cn method is introduced to rock typing, and the permeability is calculated from well log data. In the second part, we present rock-type classification when the water saturation of the formation is more than the irreducible water saturation. The novelty of this work is a simple and efficient technique to rock-type classification using the Cn method. In addition, we present a procedure to assign rock types for the transition zone using the Cn method. Moreover, this study systematically investigates the role of irreducible water saturation in rock typing. The innovation of this work lies in its ability to classify rocks in heterogeneous reservoirs for carbonate and sandstone lithology and allow for the calculation of permeability more accurately from well log data. The comparison results between the Cn method and the flow zone indicator method show the robust clustering ability of the Cn method.
ISSN:1742-2132
1742-2140
DOI:10.1093/jge/gxad056