Journal of Mining and Environement
Volume:4 Issue: 1, Winter 2013

This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values. Seismic surveying was performed next on these models. From seismic response of the models, a large number of seismic attributes were identified as candidates for pore size estimation. Classes of attributes such as energy, instantaneous, and frequency attributes were included amongst others. Applying sensitivity analysis, we determined Instantaneous Amplitude and asymmetry as the two most significant attributes. These were subsequently used in our machine learning algorithms. In particular, we used feed-forward artificial neural networks (FNN) and support vector regression machines (SVR) to develop relationships between the known attributes and pore size values in a given setting. The FNN consists of twenty one neurons in a single hidden layer and the SVR method uses a Gaussian radial basis function. Compared with real values from the well data, we observed that SVM performs better than FNN due to its better handling of noise and model complexity.

This study aims at presenting a numerical model for predicting grout flow and penetration length into the jointed rock mass using Universal Distinct Element Code (UDEC). The numerical model is validated using practical data and analytical method for grouting process. Input data for the modeling, including geomechanical parameters along with grout properties, were obtained from a case study. The effect of rock mass properties such as joint hydraulic aperture, spacing, trace length, orientation and grout properties as yield stress and water to cement, w/c ratio was considered on grout flow rate and penetration length. To illustrate the effect of aforementioned properties, models were constructed with dimensions of 40×20m. A vertical borehole with diameter of 60mm and 10m depth was drilled in a jointed rock media. The results were in a good agreement with analytical method. It was observed that by increasing joint hydraulic aperture, grouting flow increases using a power law function. The optimum grout penetration observed with joint sets intersection of 40°-60° as experienced in practice. With an increase in joint spacing grout penetration increases around borehole when spacing exceeds two meters it decreases, gradually.

Wall displacements and ground pressure acting on the lining of a tunnel increase with time. These time-dependent deformations are both due to face advance effect and to the time-dependent behavior of the rock mass. Viscoelastic materials exhibit both viscous and elastic behaviors. Thorough this study, the effect of different linear viscoelastic models including Maxwell, Kelvin and Kelvin-Voigt bodies on the behavior of tunnel is studied and the interaction of rock mass with elastic lining is analyzed. The surrounding rock mass is assumed to be homogeneous, isotropic and continuous. Hydrostatic stress field is also considered. In this paper, a series of formula for the foregoing models is driven to predict the displacement of lined and unlined circular tunnel and the pressure on the lining. The effect of lining stiffness and delay in installation of lining is analyzed. The results of new analytical relations show good correspondence with existing solutions.

Static deformation modulus is recognized as one of the most important parameters governing the behavior of rock masses. Predictive models for the mechanical properties of rock masses have been used in rock engineering because direct measurement of the properties is difficult due to time and cost constraints. In this method the deformation modulus is estimated indirectly from classification systems. This paper presents the results of a study into the application of Artificial Neural Networks (ANN) technique and Regression models for estimation of the deformation modulus of rock masses. A database, including 225 actual measured deformation modulus, Uniaxial Compressive Strengths of the rock (UCS), and Rock Mass Rating (RMR) was established. Data collected from different projects. For predicting Em by regression, a nonlinear regression method was chosen. This model showed the coefficient correlation of 0.751 and mean absolute percentage error (MAPE) of 9.911%. Also a three-layer ANN was found to be optimum, with an architecture of two neurons in the input layer, four neurons in the hidden layer and one neuron in the output layer. The correlation coefficient determined for deformation modulus predicted by the ANN was 0.786 and the quantity of MAPE was 6.324%. With respect to the results obtained from two models, the ANN technique was shown to be better than the regression model because of its higher accuracy.

The purpose of this study was to investigate the controllable operating parameters influence, including pH, solid content, collector, co-collector, and depressant dose, and conditioning time, on apatite flotation kinetics. Four first order flotation kinetic models are tested on batch flotation time-recovery profiles. The results of batch flotation tests and the fitting of first-order kinetic models to assess the influence of operating parameters on the flotation kinetics indicated that model with fast and slow - floating components and classical model gave the best and the worst fit for experimental data, respectively. Also, rectangular distribution of floatabilities and gamma distribution of floatabilities fitted the experimental data well. In this study, the model with rectangular distribution of floatabilities associated with fractional factorial experimental design was employed to evaluate the effect of six main parameters on kinetic parameters (R_∞, K). The result indicated that linear effects of depressant dose, conditioning time, and the interaction effects of solid concentration and pH statistically were important on ultimate recovery but the significant parameters for flotation rate constant were linear effects of solids content, depressant dosage and the interaction effect between pH and conditioning time. Regression equations obtained to relate between flotation operation and kinetic parameters.

Arsenic is one of the heavy metals and nearly all its compounds, especially organic compounds, are toxic. The wide spectrum of diseases caused by this element has led to evaluation of the toxicity of different arsenic species and identification of the major natural and anthropogenic pollution sources of it in the nature. Mining activities are among the main sources of anthropogenic pollution of soil and water by arsenic. The purpose of this study was geochemical modeling of different arsenic species in the wastewater of the tailings dam of Mouteh Gold processing plant in Iran to evaluate the effect of pH and temperature on the stability of these components. Modeling was done using MINTEQ software. The results showed that arsenic species at different pH values under study were H3AsO3, H2AsO3- and HAsO32-, and their actual concentration in the plant wastewater were negligible. MINTEQ software introduced H3AsO4, H2AsO4-, HAsO42- and AsO43- as arsenic V species at different pH values, of which HAsO42- and AsO43- were the main components of arsenic in plant wastewater. Given the low toxicity of arsenic V species and their easier elimination relative to arsenic III species, in the current conditions, the plant wastewater is in a good status in terms of arsenic pollution. Also temperature changes have little effect on the concentration of various arsenic species in the wastewater.

Dense Medium Cyclone is a high capacity device that is widely used in coal preparation. It is simple in design but the swirling turbulent flow, the presence of medium and coal with different density and size fraction and the presence of the air-core make the flow pattern in DMCs complex. In this article the flow pattern simulation of DMC is performed with computational fluid dynamics and Fluent software. Simulations are performed to give the axial velocity profile and the air-core. Multiphase simulations (air/water/medium) are performed with RSM model to predict turbulence dispersion, VOF model to achieve interface between air and water phases, Mixture model to give multiphase simulation and DPM model to predict coal particle tracking and partition curve. The numerical results were compared with experimental data and good agreement was observed. Also, separation efficiency of DMC was predicted using CFD simulations and shown by the Tromp curve. The comparison of simulated and measured Tromp curves showed that CFD simulation can predict Tromp curve reasonably within acceptable tolerance, however, for more accurate multiphase simulation including solid phase, it is suggested to use discrete element modeling (DEM) approach coupled with CFD.