M. Abedini; M. Ziaii; Y. Negahdarzadeh; J. Ghiasi-Freez
Abstract
The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted ...
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The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted using the image analysis techniques. The extracted features and their corresponding pore types of 682 pores were used for training two intelligent models, BPN (back-propagation network) and SAE (stacked autoencoder). The trained models take the geometrical properties of pores to classify the type of six porosity types including intra-particle, inter-particle, vuggy, moldic, biomoldic, and fracture. The MSE values for the BPN and SAE models were found to be 0.0042 and 0.0038, respectively. The precision, recall, and accuracy of the intelligent models for classifying the types of pores were calculated. The BPN model was able to correctly recognize 193 intra-particle pores out of 197 ones, 45 inter-particle pores out of 50 ones, 7 vuggy pores out of 9 ones, 10 moldic pores out of 12 ones, 2 biomoldic pores out of 3 ones, and 6 fractures out of 7 ones. Also the SAE model was able to correctly classify 193 intra-particle pores out of 197 ones, 46 inter-particle pores out of 50 ones, 8 vuggy pores out of 9 ones, 10 moldic pores out of 12 ones, 3 biomoldic pores out of 3 ones, and 7 fractures out of 7 ones. The results obtained showed that the SAE model carried out a bit more accuracy for classification of the inter-particle, vuggy, biomoldic, and fracture pores.
Exploitation
S. Safari Sinegani; M. Ziaii; M. Ghoorchi; M. Sadeghi
Abstract
In this work, the concentration gradient (CG) analysis of local-scale exploration for Porphyry-Cu deposits is applied in two zones using the G(Vz) index (CG(Zn*Pb)/CG(Cu*Mo)). The first zone is covered by a 1:2000 map of the Sungun and Astamal areas in NW Iran and the second one in the Inza area in British ...
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In this work, the concentration gradient (CG) analysis of local-scale exploration for Porphyry-Cu deposits is applied in two zones using the G(Vz) index (CG(Zn*Pb)/CG(Cu*Mo)). The first zone is covered by a 1:2000 map of the Sungun and Astamal areas in NW Iran and the second one in the Inza area in British Columbia, Canada. The rock samples are taken from Sungun and Astamal and the soil samples are taken from Inza. The Inza samples are analyzed for Cu, Pb, Zn, and Mo elements by the atomic absorption method, while the rock samples of Astamal and Sungun are analyzed for Cu, Pb, Zn, Mo, Ag, As, and Sb elements. The indices of gradient geochemical zonality (G(Vz)) of multi-elements around the mineral deposits and their spatial associations with particular geological, geochemical, and structural factors are the critical aspects that must be considered in mineral exploration. The values for the G(Vz) indices allow a distinction between the sub-ore and supra-ore anomalies, which are associated with Zone Dispersed Mineralization (ZDM) and Blind Mineralization (BM), respectively. For a comparative identification of BM and ZDM, a supra-ore (Pb*Zn) anomaly, a sub-ore (Cu*Mo) anomaly, and Vz maps are used in place of the mining geochemistry representing the supra-ore gradient anomaly, sub-ore gradient anomaly and G(Vz) map. The G(Vz) model outperforms the Vz model. The introduced technique allows for a computational distinction between the BM and ZDM ore mineralizations without exploration drilling. Prior to writing this paper, the blind porphyry-Cu mineralization was intersected at depth through borehole exploration in a highly prospective zone delineated by the G(Vz) model. The results obtained confirm the usefulness of the G(Vz) modeling for local-scale targeting of blind mineral deposits.
