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

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
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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.