Reservoir rock typing and electrofacies characterization by integrating petrophysical properties and core data in the Bangestan reservoir of the Gachsaran oilfield, the Zagros basin, Iran

Maldar, Ramin; Ranjbar-Karami, Rasoul; Behdad, Ali; Bagherzadeh, Samira

doi:10.1016/j.petrol.2021.110080

Cited by 9 publications

(1 citation statement)

References 33 publications

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“…The petroleum industry uses the hydraulic flow unit implication to improve permeability prediction for wells with coreless intervals [1]. A hydraulic unit (geometrical shape of the hole) is defined as the main volume of the entire reservoir rock, in which the petrophysical and geological properties affecting the fluid flow are constant and normally varied in different hydraulic units [2][3][4][5][6]. The change in the characteristics of the cavity geometry determines the existence of separate regions (flow units) with fluid flow characteristics.…”

Section: Introductionmentioning

confidence: 99%

Employing Statistical Algorithms and Clustering Techniques to Assess Lithological Facies for Identifying Optimal Reservoir Rocks: A Case Study of the Mansouri Oilfields, SW Iran

Eftekhari,

Memariani,

Maleki

et al. 2024

Minerals

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The crucial parameters influencing drilling operations, reservoir production behavior, and well completion are lithology and reservoir rock. This study identified optimal reservoir rocks and facies in 280 core samples from a drilled well in the Asmari reservoir of the Mansouri field in SW Iran to determine the number of hydraulic flow units. Reservoir samples were prepared, and their porosity and permeability were determined by measuring devices. The flow zone index (FZI) was calculated for each sample using MATLAB software; then, a histogram analysis was performed on the logarithmic data of the FZI, and the number of hydraulic flow units was determined based on the obtained normal distributions. Electrical facies were determined based on artificial neural network (ANN) and multi-resolution graph-based clustering (MRGC) approaches. Five electrical facies with dissimilar reservoir conditions and lithological compositions were ultimately specified. Based on described lithofacies, shale and sandstone in zones three and five demonstrated elevated reservoir quality. This study aimed to determine the Asmari reservoir’s porous medium’s flowing fluid according to the C-mean fuzzy logic method. Furthermore, the third and fourth flow units in the Asmari Formation have the best flow units with high reservoir quality and permeability due to determining the siliceous–clastic facies of the rock units and log data. Outcomes could be corresponded to the flow unit determination in further nearby wellbores without cores.

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