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The non-H atoms of the title compound, C10H8N4OS, lie approximately in a common plane (r.m.s. deviation = 0.058 Å). In the crystal, two mol­ecules are linked across a center of inversion by a pair of N—H⋯N hydrogen bonds, forming a a dimer.

The triazoloquinazoline fused-ring system of the title compound, C10H8N4O3S, is essentially planar (r.m.s. deviation = 0.027 Å). In the crystal, adjacent mol­ecules are linked by N—H⋯Osulfon­yl hydrogen bonds, generating a helical chain running along the b axis.

Electrofacies identification is a crucial procedure in reservoir characterization especially in the lack of lithofacies measurements from core analysis. Electrofacies classification is essential to improve permeability-porosity relationships in non-cored intervals. Flow Zone Indicator (FZI) is a conventional procedure for rock types classification whereas Clustering Analysis has been recently used as unsupervised machine learning technique to group a set of data objects into clusters with no predefined classes. In this paper, clustering analysis and flow zone indicator were adopted for the electrofacies characterization on a dataset obtained from incorporate of conventional core analysis and CPI logs (Effective Porosity, Water saturation and Shale volume) of three wells in the upper shale member/Zubair formation in Luhais oil field southern Iraq. The FZI attains reservoir quality evaluation through hydraulic flow unit and kozeny-carman equation, which correlates between permeability, porosity, pore throat, pore geometry and tortuosity factor. The reservoir intervals of similar or convergent range of FZI values belong to a single hydraulic flow unit and reflect the geological attributes of texture and mineralogy therefore they represent specific rock type. In clustering analysis, two approaches were adopted for electrofacies identification: k-mean and Ward's Hierarchical clustering method. These three techniques were implemented by the use of R language, which is a powerful statistical programming tool. The prepared R codes in this research can be utilized in any reservoir characterization process to define the different electrofacies The results of this research showed two implemented techniques have identified three key electrofacies with different levels of accuracy and approaches while four rock types have been predicted from the third technique. The k-mean clustering analysis was the most accurate method where each predicted electrofacies reflects the same vertical distribution of the lithofacies in the reservoir. On the other hand, the Ward's Hierarchical electrofacies prediction represents specific petrophysical properties with minor differences from real lithofacies distribution in the reservoir. Finally, electrofacies identification through the FZI technique has similar ranges of FZI values with specific porosity and permeability values. FZI technique showed difficulty in defining lithofacies boundaries within the reservoir and predicted four rock types.