International Journal of Mining & Geo-Engineering
Volume:54 Issue: 2, Summer-Autumn 2020

Spontaneous coal combustion (SCC) is one of the significant hazardous phenomena in underground coal mines. The tendency of spontaneous coal combustion is an intrinsic property, due to the presence of the maceral content. Unlike its importance, this matter has not been discussed in detail among the researchers. Therefore, it is necessary to investigate the effect of this parameter on SCC. Maceral content is defined by the original vegetation from which coal is formed. The present study examines the role of maceral content on SCC, based on 51 coal samples with different maceral contents. These samples were collected from several Iranian underground coal fields, and the R70 test was carried out on each coal sample. By examining the results and comparing the R70 values, it was found that with an increase in the vitrinite and liptinite contents and a decrease in the inertinite content, the coal samples showed to have more tendency of spontaneous combustion.

Rock physics models are widely used in hydrocarbon reservoir studies. These models make it possible to simulate a reservoir more accurately and reduce the economic risk of oil and gas exploration. In the current study, two models of Self-Consistent Approximation followed by Gassmann (SCA-G) and Xu-Payne (X-P) were implemented on three wells of a carbonate reservoir in the southwest of Iran. Then, in order to increase the accuracy and improve the efficiency of the models, a fusion model of Choquet Fuzzy Integral (CFI) was applied as a new approach. The compressionalwave velocities were estimated using two models, i.e., SCA-G and X-P, and were then integrated using the CFI fusion model. Finally, by comparing the model results and the real well log data, the Choquet model was confirmed as a compatible model with proper results. The correlation coefficient (CC) and Root Mean Squared Error (RMSE) for the estimated velocities versus the actual values showed the reliability of the constructed models.  For example, in one of the studied wells, the CC and RMSE values were 99.2 and 44 m/s, respectively, in support of the fusion model. This could be related to the optimization algorithms in the heart of the Choquet model that led to the optimization of the model parameters and also better results in the studied carbonate reservoir.

Compared to drag anchors, suction caissons (Q) in clays often provide a cost-effective alternative for jacket structures, catenary, tension leg moorings, and taut leg. In this research, two computational approaches are proposed for predicting the uplift capacity of Q in clays. The proposed approaches are based on the combinations of adaptive network-based fuzzy inference system (ANFIS) models (ANFIS-subtractive clustering (ANFIS-SC) and ANFIS-fuzzy c-means (ANFIS-FC)) with metaheuristic techniques (ant colony optimization (ACO) or particle swarm optimization (PSO)). In these approaches, the PSO and ACO algorithms are employed to enhance the accuracy of ANFIS models. In order to develop hybrid models, a comprehensive database from open-source literature is used to train and test the proposed models. In these models, d (diameter of caisson), L (embedded length), D (depth), Su (undrained shear strength of soil), θ (inclined angle), and Tk (load rate parameter) were used as the input parameters. The performance of all models was evaluated by comparing performance indexes, i.e., means squared error and squared correlation coefficient. As a result, PSO and ACO can be used as reliable algorithms to enhance the accuracy of ANFIS models. Moreover, it was found that the ANFIS– subtractive clustering-ACO model provides better results in comparison with other developed hybrid models.

An Open-Pit Production Scheduling (OPPS) problem focuses on specifying block production scheduling to achieve the highest possible Net Present Value (NPV). This paper presents a new mathematical model for OPPS under uncertainty. To this end, a robust box and ellipsoidal counterpart approach was used. The proposed method was implemented in a hypothetical model. A Genetic Algorithm (GA) and an exact mathematical modeling approach were used to solve the model. It was shown that the scheduling of deterministic and robust models in various conditions is different. Considering the type of robust counterparts, different production plans under various conditions were scheduled. Furthermore, the price of robustness was determined for various levels of conservation.

In this paper, a new method called adaptive bandwidth in the kernel function has been used for two-dimensional upscaling of reservoir data. Bandwidth in the kernel can be considered as a variability parameter in porous media. Given that the variability of the reservoir characteristics depends on the complexity of the system, either in terms of geological structure or the specific feature distribution, variations must be considered differently for upscaling from a fine model to a coarse one. The upscaling algorithm, introduced in this paper, is based on the kernel function bandwidth, written in combination with the A* search algorithm and the first-depth search algorithm. In this algorithm, each cell in its x and y neighborhoods as well as the optimal bandwidth, obtained in two directions will be able to be merged with its adjacent cells. The upscaling process is performed on artificial data with 30×30 grid dimensions and SPE-10 model as real data. Four modes are used to start the point of upscaling and the process is performed according to the desired pattern, and in each case, the upscaling error and the number of final upscaled blocks are obtained. Based on the number of coarsen cells as well as the upscaling error, the first pattern is selected as the optimal pattern for synthetic data and the second pattern is selected as the optimal simulator model for real data. In this model, the number of cells was 236 and 3600, and the upscaling errors for synthetic and real data were 0.4183 and 12.2, respectively. The results of the upscaling in the real data were compared with the normalization method and showed that the upscaling error of the normalization method was 15 times the upscaling error of the kernel bandwidth algorithm.

In this study, a simplified dissolution model has been developed to evaluate the performance of continuous leaching reactors. The model considers continuous reduction of the surface area of particles using the distribution of their size and residence time. The model was validated by the bioleaching of a pyrite-arsenopyrite concentrate in the pilot plant scale, which resulted in good agreement between the experimental data and the predicted values. The developed model was also used to predict the outlet mass density function of particles, whose results showed that the mean particle size would not necessarily decrease as the mean residence time in the leaching process decreased. Using this model, the effect of operating parameters (e.g., particle size distribution, inlet flow, reagent concentration, kinetic parameters, and the type of residence time distribution) on the reactor performance can be predicted. Therefore, the model can be used for dynamic and static analyses of leaching circuits as well as designing and optimizing the processing plants.

In open-pit mining, different designs are created, such as optimal ultimate pit limit and production planning. In order to determine the ultimate pit limit, two approaches are generally used based on geological and economic block models. In this paper, according to the long-term trend of metals price and mining costs, some suggestions were made to design the ultimate pit limit using the geological block model. In addition, a grade-based objective function was presented for determining the ultimate pit limit. Then, in order to solve the problem, a heuristic algorithm was developed to simultaneously determine the ultimate pit limit and the sequence of block mining. For a 2D geological block model, the final pit was generated using the proposed algorithm. Furthermore, to validate the generated pit limit, the results of a 3D geological block model were compared with those of the Lerchs-Grossman algorithm. The comparison showed that the two pits corresponded to each other with an accuracy value of 97.7 percent.

In a natural condition, temperature variations and phase transition of pore water are the two most effective factors on the mechanical properties of rocks. Instabilities occurred as a result of climate changes, highlight the importance of rock characteristics. This paper conducted a laboratory investigation to study the temperature-dependent mechanical behavior of rocks and to examine the quantity and quality of this relationship. In order to perform laboratory tests, a temperature-controlling apparatus was developed. Studies were conducted on 152 specimens of concrete and three types of rocks, including granite, red travertine, and walnut travertine. Then, the effect of temperature variations, from -30 to +30ºC with 10ºC intervals on the mechanical properties of the rocks, was studied. The results showed that temperature reduction, caused by pore water phase transition, improved the mechanical properties of the rocks. The maximum variation of the mean uniaxial compressive strength from +30ºC to -30ºC belonged to granite (40.1%), while the concrete specimen showed the minimum variation on the test results (33.7%). Red travertine (38.7%) and walnut travertine (34.2%) exhibited lower variations compared to granite. Also, the maximum variation in the mechanical behavior of rocks occurred between -10 and 0 °C. Additionally, variations in the mechanical properties of cracked rock samples were more than the rocks with spherical pore and the same porosity percent.

This research aims to construct 3D geophysical models of electrical resistivity and induced polarization by interpolating 2D inverted physical models through the geostatistical approach. The applicability of the method was examined for the Ghalandar porphyry-skarn copper deposit in the Agh-Daragh region, northwest of Iran. The 3D geophysical properties and block models of Cu grades were prepared by implementing the kriging interpolation method, whereby the recovered electrical models were closely linked to the Cu-sulfide mineralization. In order to evaluate the efficiency of the applied technique, the variogram models were validated using a cross-validation analysis of the kriging operation, proving the accuracy of data interpolation for each model. For the sake of meaningful correlation between geophysical models and Cu grades, the mineralization zones were extracted and subsequently propagated in the 3D space according to the generated physical properties. Meanwhile, the evaluation matrix was utilized to assess the performance of acquired results, where it confirmed that simultaneous consideration of physical models could much better determine the location of the copper mineralization. Also, the Swath plot was used as a second validation way to compare the anomalous zones.

Salt rocks have one of the most complex behaviors among different rock types due to their creep behavior.  Creep in rocks can cause a lot of undesired displacenments imposing tremendeous rehabilitation and maintenance costs to the projects.  Creep depends on many factors such as rock type, stress level, and boundary conditions in order for rock to move freely.  Amongst intrinsic factors in the rock type, the impurity of salt samples (either in gas, liquid, and/or solid form) is one of the least studied factors. This study aims to present the influence of impurity on the creep behavior of the salt rocks of the Hormuz series, as the case study. This series is one of the oldest evaporitic deposits in the world resulted in more than 350 salt domes in Iran and other parts of the Middle East. Unfortunately, there has been no comprehensive rock mechanics study on the Hormuz salt rocks so far. In this study, a few recovered cores were obtained and prepared from the exploration boreholes drilled in this formation, and the creep parameters were determined using laboratory tests. Also, the effect of impurity percentage on the creep properties of the Hormuz salt rocks was investigated. Since in salt rock masses the purity percentage is different, impurity affects the creep behavior. The tested samples were categorized into seven different groups, based on the quantity of the impurity, which consists mainly of anhydrite and quartzite. Laboratory tests showed that the uniaxial compressive and tensile strength values increase by increasing the solid impurity in the samples. In contrast, the maximum and instantaneous strains reduce by increasing the percentage of impurities in different stages of the creep test. Increasing the amount of impurity in pure samples led to increasing Burger's parameters. Also, it was observed that obtaining creep parameters from laboratory test results with mathematical approximation method had fewer errors compared to the manual method explained by Goodman. This is worth for the development of underground mining operations in salt structures. Accurate recognition of creep properties might have a considerable impact on the design as well.</span>In the present study, the effect of impurity percentage is investigated on the creep properties of Hormuz salt rock. Because in salt rock masses, purity percentage is different, impurity and its amounts affects creep behavior. The tested samples were categoried into seven different groups based on the quantity of the impurity (which consists mainly of anhydrite and quartzite). Laboratory tests showed that the uniaxial compressive strength and tensile strength increase by increasing solid impurity in the samples. In contrast, the maximum strain and instantaneous strain reduce by increasing the percentage of impurities in different stages of creep test. Increasing amount of impurity in pure sample led to increasing Burger's parameters. Also it was observed that obtaining creep parameters from laboratory test results with mathematical approximation method has less error than the manual method explained by Goodman. This might be worth noticing because for development of underground mining operations in relation with salt structures, accurate recognition of creep properties might have considerable impact on the design.

Twenty measured blast data from the Golegohar iron mine (southern Iran) were used to generalize nonlinear models for the estimation of dominant frequencies of blast waves using rock mass, explosive characteristics, and blast design. The imperialist Competitive Algorithm (ICA) was used to determine the nonlinear regression model coefficients. Possessing a good correlation coefficient, the proposed model can be directly used for predicting blast-induced dominant frequencies of waves. The determination coefficient (R2</sup>) found by the ACI-based nonlinear model was 0.98 for frequency, while that of the traditional Multivariate Linear Regression Model (MVLRM) was 0.89. Also, according to the calculation of other well-known statistical errors between the estimated and real measured values of frequency, ICA-based models have higher Variance Account for (VAF) value, as well as lower values of Route Mean Square Error (RMSE), Variance Absolute Relative Error (VARE), Median Absolute Error (MEDAE), and Mean absolute percentage error (MAPE)compared to the linear model. It was found that the proposed nonlinear model is more accurate and capable of estimating the values of the dominant frequency of blast waves.

Designing and optimizing the dimensions of drainage systems is very important for keeping the downstream shells dry and preventing the increase of pore water pressure in the body of earth dams. By optimizing the drainage dimensions, the minimum factor of safety, and consequently the construction costs, can be reduced. The purpose of this research was to optimize the size of horizontal drainage that is affected by some important parameters of the dam. In this study, a homogeneous earth dam was modeled using the Geostudio software. The minimum factor of safety was obtained by changing drainage dimensions, materials, and the slope of the dam body. A two-layer neural network was used to predict the least factor of safety resulted from different scenarios created in the software. By training the neural network based on the data obtained from homogeneous dams, the minimum factor of safety for drainage optimization was extracted. For optimal, an Mfile was fitted to the trained neural network function, by which the optimal values of the dam parameters were calculated.The results showed that the optimum values of drainage dimensions obtained for homogeneous dams for three heights of 10, 20, and 30 m could be generalized to other heights between 10 and 30 m with a simple interpolation.

Well integrity is defined as the application of technical and operational solutions to reduce the uncontrollable risk of fluids leakage in the well lifetime. In any drilling and production operation, lack of knowledge about geomechanical behavior of the surrounding formations is considered as a major risk. Therefore, in-situ stress conditions and mechanical properties of formations are important factors in well integrity studies. In this paper, a 3D finite element model was built to simulate the integrity of wells. An FEM analysis was used to investigate the plastic deformation in cement and the Von Mises failure criterion inside the casings under different stress conditions, and to study the mechanical properties of the formation. A clear increase in plastic strain in the cement and Von Mises stress inside the casings was observed with increasing the ratio of horizontal to vertical stress in orthotropic and isotropic conditions as well as with increasing the difference between horizontal stresses in anisotropic conditions. When conducting the translation error sensitivity analysis, the impact of major mechanical parameters of the formation was evaluated as well. The results showed that by increasing Young's modulus, cement became hard and brittle. Meanwhile, an increase in the Poisson ratio led to plastic behavior. The maximum plastic strain was found at the cement-casing boundary due to the presence of a lower cement-formation friction value. The highest Von Mises stress value in the casings was also produced parallel toward the minimum horizontal stress. Additionally, with an increase in the cohesion and friction angle of formation, the cement became harder, and consequently, the safety factor for the casings increased.