A preliminary C-O stable isotopes geochemical characterization of several nonsulfide Zn-Pb Tunisian deposits has been carried out, in order to evidence the possible differences in their genesis. Nonsulfide ores were sampled from the following deposits: Ain Allegua, Jebel Ben Amara, Jebel Hallouf (Nappe Zone), Djebba, Bou Grine, Bou Jaber, Fedj el Adoum, Slata Fer (Diapir Zone), Jebel Ressas, Jebel Azreg, Mecella (North South Axis Zone), Jebel Trozza, Sekarna (Graben Zone). After mineralogical investigation of selected specimens, the C-O stable isotopic study was carried out on smithsonite, hydrozincite, cerussite and calcite. The data have shown that all the carbonate generations in the oxidized zones of Ain Allegua and Jebel Ben Amara (Nappe Zone), Bou Jaber, Bou Grine and Fedj el Adoum (Diapir Zone), Mecella and Jebel Azreg (North South Zone) have a supergene origin, whereas the carbonates sampled at Sekarna (Graben Zone) (and in limited part also at Bou Jaber) precipitated from thermal waters at moderately high temperature. Most weathering processes that controlled the supergene alteration of the Zn-Pb sulfide deposits in Tunisia had probably started in the middle to late Miocene interval and at the beginning of the Pliocene, both periods corresponding to two distinct tectonic pulses that produced the exhumation of sulfide ores, but the alteration and formation of oxidized minerals could have also continued through the Quaternary. The isotopic characteristics associated with the weathering processes in the sampled localities were controlled by the different locations of the sulfide protores within the tectonic and climatic zones of Tunisia during the late Tertiary and Quaternary.

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This paper discusses the use of a novel data-driven method for automated facies classification and characterization of carbonate reservoirs. The approach makes an extensive use of wireline and while drilling electrical borehole image logs and provides a direct and fast recognition of the main geological features at multi-scale level, together with secondary porosity estimation. This embodies an unbiased and valuable key-driver for rock typing, dynamic behavior understanding and reservoir modeling purposes in these puzzling scenarios. The implemented methodology takes advantage of a non-conventional approach to the analysis and interpretation of image logs, based upon image processing and automatic classification techniques applied in a structural and petrophysical framework. In particular, the Multi-Resolution Graph-based Clustering (MRGC) algorithm that is able to automatically shed light on the significant patterns hidden in a given image log dataset. This allows the system to perform an objective multi-well analysis within a time-efficient template. A further characterization of the facies can be established by means of the Watershed Transform (WT) approach, based on digital image segmentation processes and which is mainly aimed at quantitative porosity partition (primary and secondary). The added value from this data-driven image log analysis is demonstrated through selected case studies coming from vertical and sub-horizontal wells in carbonate reservoirs characterized by high heterogeneity. First, the MRGC has been carried out in order to obtain an alternative log-facies classification with an inherent textural meaning. Next, the WT-based algorithm provided a robust quantification of the secondary porosity contribution to total porosity, in terms of connected vugs, isolated vugs, fractures and matrix contribution rates. Finally, image log-facies classification and quantitative porosity partition have been integrated with production logs and pressure transient analyses to reconcile the obtained carbonate rock types with the effective fluid flows and the associated dynamic behavior at well scale. The presented novel methodology is deemed able to perform an automatic, objective and advanced interpretation of field-scale image log datasets, avoiding time-consuming conventional processes and inefficient standard analyses when the number of wells to be handled is large and/or in harsh circumstances. Moreover, secondary porosity can be proficiently identified, evaluated and also characterized from the dynamic standpoint, hence representing a valuable information for any 3D reservoir models.