Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods.

Electrofacies analysis conducted the distribution effects throughout the reservoir despite the difficulty of characterizing stratigraphic relationships. Clustering methods quantitatively define the reservoir zone from non-reservoir considering electrofacies. Asmari Formation is the most significant reservoir of the Mansouri oilfield in SW Iran, generally composed of carbonate and sandstone layers. The stratigraphical study is determined by employing 250 core samples from one exploratory well in the studied field. Five zones with the best reservoir quality in zones 3 and 5 containing sandstone/shale are determined. Moreover, multi-resolution graph-based and artificial neural network clustering involving six logs are employed. Utilizing Geolog software, an optimal model with eight clusters with better rock separation is obtained. Eventually, five electrofacies with different lithological compositions and reservoir conditions are identified and based on lithofacies describing thin sections, sandstone, and shale in zones 3 and 5 show high reservoir quality. According to the depth related to these zones, most of the facies that exist in these depths include sandstone and dolomite facies, and this is affected by the two factors of the primary sedimentary texture and the effect of the diagenesis process on them. Results can compared to the clustering zone determination in other nearby sandstone reservoirs without cores.

Hydraulic flow unit and rock types of the Asmari Formation, an application of flow zone index and fuzzy C-means clustering methods.

Rock types are the reservoir's most essential properties for special facies modeling in a defined range of porosity and permeability. This study used clustering techniques to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units (HFUs) were determined for studied data utilizing histogram analysis, normal probability analysis, and the sum of squared errors (SSE) statistical methods. Then, two flow zone index (FZI) and fuzzy c-means (FCM) clustering methods were used to determine the rock types in the given well according to the results obtained from the HFU continuity index acts in-depth. The FCM method, with a continuity number of 3.12, compared to the FZI, with a continuity number of 2.77, shows more continuity in depth. The relationship between permeability and porosity improved considerably by utilizing HFU techniques. This improvement is achieved using the FZI method study. Generally, all samples increased from 0.55 to 0.81 in the first HFU and finally to 0.94 in the fourth HFU. Similar flow properties in an HFU characterized the samples. In comparison, the correlation coefficients obtained in the FCM method are less than those in the general case of all HFUs. This study aims to determine the flowing fluid in the porous medium of the Asmari reservoir employing the c-mean fuzzy logic. Also, by determining the facies of the rock units, especially the siliceous-clastic facies and log data in the Asmari Formation, the third and fourth flow units have the highest reservoir quality and permeability. Results can be compared to determining HFU in nearby wellbores without cores.