Exploration
Hasan Feizi Anhar; Ali Imamalipour; Peyman Afzal
Abstract
Geochemical zoning is a key concept in exploration geochemistry. It provides an effective means of predicting the erosion level of mineralization, distinguishing supra-ore from sub-ore halos, and identifying concealed ore bodies. While classical geochemical zoning methods have been widely applied for ...
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Geochemical zoning is a key concept in exploration geochemistry. It provides an effective means of predicting the erosion level of mineralization, distinguishing supra-ore from sub-ore halos, and identifying concealed ore bodies. While classical geochemical zoning methods have been widely applied for decades, this study introduces an enhanced three-dimensional geochemical zoning model specifically tailored for the Sungun porphyry deposit, based on geochemical data obtained from 264 drill cores comprising a total of 33,368 rock samples. The model is constructed using ratios of factors derived from Staged Factor Analysis (SFA) of ore-related major (Cu and Mo) and minor (Cd, Mn, Pb, Zn, and Ag) elements, and further refined through fractal modeling for classification. Fractal modeling method (C-V) clearly shows four distinct populations and three breakpoints, which to supergene (0.9–1.4%), hypogene (0.6–0.9%), and oxidized zones (0.1–0.6%). The application of the method to the Sungun porphyry system reveals a strong spatial correlation between the zoning index, copper grade distribution, and alteration patterns. SFA effectively separates supra-ore and sub-ore elements, while fractal modeling improves the robustness of zoning classification. Integration of the developed 3D zoning index with copper grade models reveals a clear structural relationship among alteration, geochemical ratios, and copper distribution. The proposed approach enhances the resolution of porphyry deposit zoning, offering improved targeting accuracy and reduced risk in deep drilling exploration.
Exploration
Sina Samadi; Peyman Afzal; Mehran Arian; Ali Solgi; Zahra Maleki; Mohammad Seraj
Abstract
An important work for fractured reservoir modeling and development of oilfields is the delineation of geomechanical attributes such as permeability. The main aim of this research work is detection of permeability zones in the Asmari reservoir of Gachsaran oilfield (SW Iran) based on mud loss data. The ...
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An important work for fractured reservoir modeling and development of oilfields is the delineation of geomechanical attributes such as permeability. The main aim of this research work is detection of permeability zones in the Asmari reservoir of Gachsaran oilfield (SW Iran) based on mud loss data. The mud loss was 3D estimated by ordinary kriging method. Then, fractal number-size, concentration-volume, and concentration-distance to fault models were applied for permeability zone classification. The concentration-distance to fault fractal model shows three permeability zones, and the concentration-volume fractal modeling represents eight zones with an index multifractal behavior. Moreover, the number-size fractal analysis presented that a multifractal behavior with five societies. The correlation between the results obtained by these fractal methods reveals that the obtained zones have a proper overlap together. High value permeability zones based on the concentration-distance to fault and concentration-volume fractal models are began from 501 Barrel Per Day (BPD) mud loss, and 630 BPD obtained by the N-S modeling. Fractal modeling indicates that the permeability zones occur in the SW, NW and southern parts of the Gachsaran oilfield which can be the fractured section of the Asmari reservoir rock. Main faults from this oilfield are correlated with the permeability zones derived via fractal modeling.
Hossein Mahdiyanfar; Amir Salimi
Abstract
This work aims to investigate the geochemical signatures of the Cu porphyry deposit in the Dalli area using the geochemical soil samples. At the first step, the geochemical data was opened using the Centered Log-Ratio (CLR) transform method. Then those outlier samples that reduce the accuracy of the ...
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This work aims to investigate the geochemical signatures of the Cu porphyry deposit in the Dalli area using the geochemical soil samples. At the first step, the geochemical data was opened using the Centered Log-Ratio (CLR) transform method. Then those outlier samples that reduce the accuracy of the geochemical models were detected and removed using the Mahalanobis Distance (MD) method. We applied the Principal Component Analysis (PCA) and Geochemical Mineralization Prospectivity Index (GMPI) methods on the cleaned transformed geochemical dataset. The PCA method identified five principal components (PCs), from which PC1 including Cu, Au, and Mo, are specified as the mineralization factor (MF). The GMPI approach can improve the multivariate geochemical signature in geochemical mapping. Hence, the GMPI values of the samples were calculated based on the score values of MF (Cu, Au, Mo). The results convey that the large values of GMPI (MF) (Cu, Au, Mo) strongly correlate with the quartz diorite porphyry rocks and potassic alteration zones. The GMPI (MF (Cu, Au, Mo)) index was modeled using the Concentration-Number (C-N) fractal method. The C-N fractal model identified four geochemical populations based on the different fractal dimensions. The geochemical anomaly map of GMPI (MF) (Cu, Au, Mo) was delineated using these classified populations. The obtained promising areas were validated adequately by more detailed exploration works and deep drilled boreholes as well. The Cu-Au mineralization potential parts are appropriately mapped by this hybrid method. The results obtained demonstrate that this scenario can be adequately used for geochemical mapping on local scales.
