p. afzal

In the seismic methods, estimation of the formation pressures is obtained by converting the seismic velocity to the pore pressure, and comparing it with the effective pressure during the well-test program. This work is a new challenge regarding the velocity study domain in an oil field in SW Iran. The reservoir generally consists of carbonate rocks, and contains no shale interbeds. Here, 23 well information, seismic data interpretation, compressional (Vp), and shear velocity (Vs) models are implemented. The models are determined from the combined geo-statistical methods, and the results obtained are compared with the fractal models. The final Vs cube is modeled in order to determine the formation fracture pressure using the exploratory well cores and dipole sonic imager (DSI) Vs logs with a correlation coefficient of 0.95 for the Vs data obtained from the porosity, lithology, and primary DSI data. The vertical seismic profiling (VSP) data introduce a maximum interval velocity of 2760-2900 m/s in the field related to the Gotnia formation. The final amounts ​​of seismic acoustic impedance inversion (AI) at the bottom of the field are mostly in the range of 8000-15000 [(m/s)*(g/cm3)], which can be related to the calcareous formations. Based on the Logratio matrix obtained from the fractal velocity-volume (Vp-V) model, the maximum overall accuracy (OA) in the dominant limestone intervals is 0.74. It indicates a high correlation of the Vp cube model obtained from the combination of sequential Gaussian simulation (SGS) and co-kriging models with AI. The uncertainty studies of Vp model in blind wells are about 50%, which is acceptable considering the large well numbers.

Signal analysis approaches are a powerful and widely used tool in processing multi-spectral satellite images for detection of alteration zones. The main goal of this work is application of the spectrum-area fractal methodology based on the Landsat 8 OLI satellite images’ data for separation alteration zones for iron oxides at the Tarom region (NW Iran). These alteration zones, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NWDI) are detected using the band-ratio and band combination methods. Then the calculated values are categorized by Spectral Angle Mapper (SAM), k-means, and S-A fractal model. Considering a positive correlation of iron oxides alterations along with magnetite mineralization as an index of mineralization at the studied region, the promising areas are classified by a decision-making model using the TOPSIS method with an acceptable accuracy for presenting in the exploration models.

The goal of this research work is to recognize the metallic mineralization potential in the Ahar 1:100,000 sheet (NW Iran) using the remote sensing data based on determination of the alteration zones. This area is located in the Ahar-Arasbaran metallogenic zone as a significant metallogenic zone in Iran and Caucasus. In this research work, the Landsat-7 ETM+ and advanced space borne thermal emission and reflection radiometer (ASTER) multispectral remote sensing data was interpreted by the least square fit (LS-Fit), spectral angle mapper (SAM), and matched filtering (MF) algorithms in order to detect the alteration zones associated with the metallic mineralization. The results obtained by these methods show that there are index-altered minerals for the argillic, silicification, advanced argillic, propylitic, and phyllic alteration zones. The main altered areas are situated in the SE, NE, and central parts of this region.

The studied area in this research is the Esfordi phosphate mine in the Bafq metallogenic state, which apatite, magnetite and hematite are its main mineralization. As one of the high phosphorus iron – apatite mine in the region, this mine contains significant mineralization of rare earth elements with a maximum and average grade of 1.7% and 0.5% (as monazite, zircon and allanite minerals in apatite), and has LREE enrichment and Eu negative anomaly.  In order to primary assessment of REEs concentration status in study area, the outlook coefficient of REEs (Koutl), which is based on the classification of REEs by the level of industry demand, was used. This coefficient is the ratio of the amount of critical REEs (Dy, Tb, Nd, Y, Er and Eu) to the amount of surplus REEs (Ce, Ho, Tm, Yb and Lu), which can be used to investigate mineralization of different types in newly discovered deposits and active mines. The results indicate the highest outlook coefficient (Koutl=0.67) for microgranite to rhyolite with amphibole unit, and also promising average outlook coefficient of 0.53. The Esfordi mine despite the lower grade, it is closer to the ideal composition compared to China's Bayan Obo mine (Koutl=0.2). Thus, can portend a bright future for REEs in this mine, if focus on exploration of these elements and further geochemical and mineralogical investigations are carried out.

This work aims to classify the gas content zones for coking coal deposits using a Number-Size (N-S) fractal modeling considering the explosive and free gas data. The case study is the C1 coking coal seam in the Parvadeh-4 coal deposit in the central Iran. Following this, the N-S log-log plots are created, which indicate three populations regarding both the explosive and gas data exist. Proper zones for both data in the C1 coking coal seam have explosive and free gas contents lower than 9.5 m3/ton and 1.3 m3/ton, respectively. The low-value gas content zone is located in the western part of the studied area, which is in the lowest depth of this coal seam. In addition, a high-value content zone exists in the E, NE, and SW parts of this area with explosive and free gas contents higher than 13.8 m3/ton and 2.2 m3/ton, respectively. These parts of the C1 seam are dangerous due to a high volume of gas content. Moreover, the explosive and free gas contents have a positive correlation with high risk gas volume based on the famous standards.

It is significant to discover a global optimization in the problems dealing with large dimensional scales to increase the quality of decision-making in the mining operation. It has been broadly confirmed that the long-term production scheduling (LTPS) problem performs a main role in mining projects to develop the performance regarding the obtainability of constraints, while maximizing the whole profits of the project in a specific period. There is a requirement for improving the scheduling methodologies to get a good solution since the production scheduling problems are non-deterministic polynomial-time hard. The current paper introduces the hybrid models so as to solve the LTPS problem under the condition of grade uncertainty with the contribution of Lagrangian relaxation (LR), particle swarm optimization (PSO), firefly algorithm (FA), and bat algorithm (BA). In fact, the LTPS problem is solved under the condition of grade uncertainty. It is proposed to use the LR technique on the LTPS problem and develop its performance, speeding up the convergence. Furthermore, PSO, FA, and BA are projected to bring up-to-date the Lagrangian multipliers. The consequences of the case study specifies that the LR method is more influential than the traditional linearization method to clarify the large-scale problem and make an acceptable solution. The results obtained point out that a better presentation is gained by LR–FA in comparison with LR-PSO, LR-BA, LR-Genetic Algorithm (GA), and traditional methods in terms of the summation net present value. Moreover, the CPU time by the LR-FA method is approximately 16.2% upper than the other methods.

Detection of mineralized zones based on ores and gangues is important for mine planning and excavation operation. The major goal of this research work was to determine the zones based on ores and gangues by a combination of fractal and factor analysis in the Chah Gaz iron ore (Central Iran). The Concentration-Volume (C-V) fractal method was carried out for Fe, P and S, which indicated that the main mineralized zones consisted of the Fe, S, and P values ≥ 57%, ≤ 0.4%, and ≤0.3%, respectively. Factor analysis categorized variables in two groups including factor 1 (F1) and factor 2 (F2) for ore and gangue, respectively. The C-V fractal modeling on the derived factors showed four zones for F1 and F2. Based on the correlation among the results of fractal modeling on the elements and factors, the first and second zones of F1 were proper for exploitation. Furthermore, the last and first zones of F1 and F2 could be assumed as the main waste for mining excavation.