IRANIAN JOURNAL OF MINING ENGINEERING (IRJME)
Year:2022 | Volume:17 | Issue:54|Papers Count:7

Determination of petrophysical parameters and their distribution in the reservoir can lead to new zonation and change of production thickness zone. clay minerals exist in most of oil reservoirs and reduce important parameters such as porosity, permeability and production potential. The purpose of this study was to investigate the petrophysical properties of Asmari Formation by combination of different traditional petrophysical methods for volume of clay estimation and reservoir evaluation studies. The traditional calibration of gamma ray log such as Bhuyan – Passey, Larionov-1, Steiber, Clavier and Jozanikohan relationships were applied which resulted to 45% relative error for estimation of clay minerals in compare to the 15 known laboratory values of this parameter. In the next step, neural network modeling was performed to reduce relative error. 259 data were estimated from laboratory values and trained with Tangent Sigmoid Activation Function, Levenberg-Marquardt training algorithm, 6 neurons and 1 hidden layer in a MLP neural network. The clay volume outputs of the neural network were classified and the body of the reservoir determined to be sandstone-clay. By investigating the density-porosity cross-plots, formation lithology and good quality reservoir intervals were introduced for Perforation operation. The gamma-ray data and neutron porosity data were also categorized to give the low to high quality intervals. Finally, by combination all the results in this study, the quality of Asmari formation were estimated to be "good".

Mine fleet management has a significant impact on increasing profitability and productivity. Also failure to meet production planning targets, increasing delays and waiting times requires monitoring to improve their performance. In this research, using Arena software base on discrete event simulation technique, modeling and analysis of current performance of loading and haulage operations performed for waste transportation in Mehdi Abad lead &zinc mine. Then appropriate improvement approaches suggested. For this purpose, first by analyzing production operations, trucks and loaders were identified as simulation entities and resources. Then, mine pit, repair shops and waste dump were defined as model subsystems. After analyzing of collected data of loading, haulage and navigating times of mine roads, the distribution functions of each operation estimated with the Input Analyzer tool. According to the simulation results, the loader productivity of mine is low, which will increase as the depth of the mine increases. Haulage road expansion. Then, the behavior of the system was simulated with two scenarios were defined including decreasing of overloaded loaders and adding trucks in 63 different modes. According to the results of the study, doubling the number of 30-ton trucks will maximize both the average service of loaders and trucks. On the other hand, it is possible to remove 2 loaders without significantly altering the performance of the remaining active loaders.

Image processing of remote sensing for separation hydrothermal alteration, in the case of missing initial spectra of pixels, can be a challenge for the researcher. The previous researches has shown that accurate separation of hydrothermal alteration zones using Conventional methods of image processing based on Spectral Properties of pixels, is not possible. Therefore, this research is trying to present a multi-stage algorithm that identify and discriminate the hydrothermal alteration zones in western part of Kerman province with high accuracy. To achieve this goal, the principal component analysis method, Fractal Concentration-Area Model and Full Index Kriging (FIK) geostatistical model are used in combination. The results show the high accuracy of the FIK model in identifying and separating each of the phyllic, argillic and propylitic alterations in the study area. Also, to evaluate the classification error, the Confusion matrix was investigated. The results of the Confusion matrix showed that the FIK model performs well in terms of image classification. Also, given the high number of training pixels in the phyllic zone, the FIK model has been able to identify this type of alteration very well.

Preservation and remediation of historic buildings and building Stones have always been significant challenges for mining engineers. One of the useful methods to prevent stone decade is filling the pores using microbial induced calcium precipitation (as known bio-grouting). In this paper, the effects of different conditions on the optimal use of bio-grouting are investigated by performing laboratory tests. Unprocessed travertine samples with the same mineralogical and lithological conditions were selected. Experimental pattern with four variables (concentrations of calcium chloride and urea in cemention solution, pH of washing solution and ambient temperature) were designed to determine the wave velocity ratio (as a remediation sign) at samples. The input variables were selected in 5 levels using response surface designing method and 31 laboratory tests were performed. The results indicate that the use of equal concentrations of calcium chloride and urea (about 1 M) in the cementation solution is more effective for microbial precipitation. Adjusting the pH of the washing solution about 7 and the ambient temperature about 15 Celsius degrees will lead to optimal amount of calcium carbonate precipitatation, resulting in better rock remediation. Increasing the pH from 7 to 11, reduces the wave velocity ratio by 35% and decreasing the pH from 7 to 3 reduces the wave velocity ratio by 40%. Also, increasing and decreasing the ambient temperature from 15 to 30 and 15 to 0 Centigrade degrees, respectively, reduces the wave velocity ratio by 15%. The above results show that very acidic or alkaline environments and very cold or warm temperatures are not suitable for microbial precipitation operation and the effect of acidic rains on the degradation of remediating precipitations is more than alkaline rains.

The bioavailability of potentially harmful elements such as heavy metals has been ignored in many researches, and most studies are conducted based on total concentration. Considering that the total concentration of metals, in most cases, shows limited information about the mobility and bioavailability of heavy metals, therefore, partial concentration or extraction is the best method in estimating the content of metals in soil. In this study, 40 samples were collected from surface soils around the mine, tailing and agricultural soils around the Ahangaran mine. Total concentrations of lead and zinc were determined by ICP-OES, and the bioavailable fraction was carried out by single-stage method extraction (0. 1N HCl) using atomic absorption spectroscopy (AAS). The contamination factor (CF) results for Pb in tailing and around the mine areas indicate very high contamination and moderate contamination for agricultural soils. The amount of CF for Zn presents a very high contamination factor in tailings and moderate contamination factor in the other areas. Pollution load index (PLI) values show that the tailings and soils around the mine are extremely polluted, and agricultural soils are moderately polluted. The Risk Assessment Code (RAC), which was calculated based on zinc and lead bioavailability, presented moderate risk for zinc in all study areas and low risk and for Pb (except agricultural soils that show moderate risk). Low bioavailable values indicate the presence of Pb and Zn in the residual phase (in the form of minerals) that do not pose much risk to living organisms. Increasing the amount of bioavailability in agricultural soils located at farther distances from the mine indicates the presence of these metals in carbonate and exchangeable phases.

Studying the shear behavior of rock joints due to its significant effect on the stability of structures is very important. In this regard, important and valuable works have been done. Many empirical and theoretical models have been proposed to estimate the joint shear strength. The ultimate goal is to predict the joint shear strength through known parameters without carrying out tests. This study investigates the shear behavior of natural rock joints obtained from core drilling without filling materials. For this purpose, the surface morphological characteristics of the natural joints were captured by Close-Range Photogrammetry. The direct shear tests were performed on the Constant Normal Load condition. The Gene Expression Programming algorithm was used to obtain the relationships between variables. In order to model 70% of data were used to train and the remaining 30% of data to test. Overall, four models were run and a mathematical relationship was presented to estimate the shear strength of natural rock joints. To evaluate the efficiency of the models, valid criteria were used such as: R2, MSE, MAE, RSME. The results showed that the GEP algorithm has an appropriate accuracy for the estimation of the output variable.

Bentonite is an industrial soil with thousands of applications. Most of the Iranian bentonite mines, especially the mines of South Khorasan province, which is the center of the Iranian bentonite, are operating in a semi-closed or limited operation due to lack of reserves and dispersion. The construction of mobile processing plants will boost these mines, create added value, create jobs in deprived areas and make proper use of the country's reserves. In addition, flexibility, reducing the cost of transporting raw materials, the need for low investment, quick and easy installation and improving the knowledge of equipment manufacturing and mining are also the most important advantages of these processing plants. A mobile processing plant to produce bentonite concentrate requires primary crushing (jaw crusher), secondary crushing (impact crusher), heavy intermediate separator (cyclone or Dyna whirlpool), drying (rotary dryer), grinding (roller mill), packaging, power supply and control and monitoring units, that each of them can be mounted on a trailer. Technical and economic studies show that such a mobile processing plant with a capacity of 200 tons per day requires about 530 billion rials of fixed capital and about 30. 5 billion rials of working capital. Also, the annual operating costs of this project are estimated at about 180 billion rials. With these specifications, the plan will have a payback period of 1. 8 years, a positive net present value and an internal rate of return of 48%, which is appropriate and cost-effective in terms of investment. Also, the most important risks of this plan in terms of investment include operating processes, exchange rate fluctuations and prices. Other parameters of this plan are medium and low risk.