This paper proposed a practical approach for reliability analysis of vertical cut in unsaturated soil. This approach extends the application of conditional random field into the unsaturated soils. A real case study of vertical cut was considered and three boreholes were drilled to investigate the subsurface layers. The Sequential Gaussian Simulation (SGS) was used to generate conditional random field with considering the possible fluctuation of soil properties between known data. The undersampled parameters were estimated by cokriging method, while Kriging method was used to estimate other stochastic parameters. In order to verify the efficienty of simulations, it was checked that all data were reproduced at their locations and the input semivariogram model was reproduced within acceptable fluctuations. To predict the unsaturated soil behaviour, the Soil Water Retention Curve (SWRC) was estimated using physico-empirical method with the aim of determining suction stress for finite element stability analysis. The vertical cut was analyzed with and without considering suction. It was concluded that considering unsaturated condition shifts the mean of safety factor from the unsafe ranges to the safe ranges. It was illustrated that the number of known data effects the construction of conditional random fields and leads to different probability of failure.

In mining projects, all uncertainties associated with a project must be considered to determine the feasibility study. Grade uncertainty is one of the major components of technical uncertainty that affects the variability of the project. Geostatistical simulation, as a reliable approach, is the most widely used method to quantify risk analysis to overcome the drawbacks of the estimation methods used for an entire ore body. In this work, all the algorithms developed by numerous researchers for optimization of the underground stope layout are reviewed. After that, a computer program called stope layout optimizer 3D is developed based on a previously proposed heuristic algorithm in order to incorporate the influence of grade variability in the final stope layout. Utilizing the sequential gaussian conditional simulation, 50 simulations and a kriging model are constructed for an underground copper vein deposit situated in the southwest of Iran, and the final stope layout is carried out separately. It can be observed that geostatistical simulation can effectively cope with the weakness of the kriging model. The final results obtained show that the frequency of economic value for all realizations varies between 6. 7 M$ and 30. 7 M$. This range of variation helps designers to make a better and lower risk decision under different conditions.

Porosity is a petrophysical property used for reservoir 3D modeling and prediction of production scenarios for the purpose of economic decisions play an important role in field and reservoir management. In this study, drilling data (logs) from 3 wells and geostatistical method including sequential Gaussian simulation were used to predict porosity pertophysical property. Sequential Gaussian simulation is widely used for porosity model construction since it is easily applied and is flexible in real heterogeneous conditions. In this method, with the available data including values and coordinates values of the same parameter in unknown points can be predicted. In Sequential Gaussian method cells having certain values from petrophysical parameters are taken into account. After, constructing reservoir structural model and analyses of petrophysical data, simulations models for 5 reservoir units of Sarvak and Dariyan reservoirs (1, 2, 3 and 4) in the studied filed were established for porosity with high precision.

The most important properties of Geostatistical Simulation are producing a group of images, which shows a range of possible events, calculating probable percentage of happening and also determining the risk in each step of process. 137 holes are drilled in this deposit. 3331 data from 125 drilled holes are gathered for grade estimation and reserve evaluation. Statistical studies show that Fe grade is not skewed and obey a natural model.plotting the empirical variogram in different directions show neither geometric nor regional anisotropy for the deposit. For simulating via SGS method, data are transferred to standard normal and then simulated 100 times (in this way 100 realizations were created). All of the realizations were honor to histogram and variogram of samples, so all realizations are valid. E_Type and probability maps are drawn in 12.5 meters intervals and grade-tonnage curves were drown for each realization. E_Type maps evaluate average 108 million tones with 56% Fe content for whole deposit. Grade-tonnage curves were showed the range of tonnage variance. That is between 97 and 116 million tones for whole deposit.

In this paper a hybrid approach of Sequential Gaussian Simulation (SGSIM) and Concentration-Volume (C-V) fractal method was applied to boreholes data for classification of major geochemical parameters and associated features with alteration zones of the Haftcheshmeh Cu deposit in NW of Iran. Thereupon, Cu parameters were detected with higher efficiency and lower uncertainty. The purpose is also extended to delineate the alteration zones pertinent to Cu mineralization. Firstly, the most straightforward simulation (SGSIM) was utilized for projecting different lithogeochemical parameters of Cu. Then the C-V fractal model was used to discriminate these parameters by thresholds, obtained from the C-V Log-Log plot. The Fractal based resulting maps of 10 realizations and their E-type indicate their association with potassic alteration that has imposed on porphyry granodiorite. Moreover, these maps illustrate that the boreholes (1, 9, 23, and 31) at about longitudes of 643400 to 643800 are more promising than others. This fact explicitly had been correlated with reality of the studied area and denoted in its primary surface map. The results based on SGSIM, C-V confirmed enhanced mineralization in three-dimensional maps of the Haftcheshmeh deposit as a powerful combined method that can be used to detect the similar ore zones in continuation of the ore roots in adjacent areas.

Estimation of petrophysical parameters of hydrocarbon reservoirs such as porosity and permeability using 3D seismic data is considered as an efficient and effective tool for comprehensive study of reservoirs as well as reservoir management. In this study, which was carried out on one of the oil fields in the Persian Gulf, the aim is to simulate the petrophysical parameters of effective porosity and permeability by using Co-Sequential Gaussian Simulation in part of Middle-Sarvak reservoir. With this simulation, a three-dimensional model of petrophysical reservoir parameters can be presented which is important for simulating fluid flow and identifying areas that are prone with higher reservoir quality. For this purpose, effective porosity and permeability logs of seven wells with a 3D seismic cube and seismic inversion results have been used. After reservoir gridding and creating a structural model, up scaled petrophysical data has entered to a model. For three-dimensional distribution of effective porosity parameter, due to the correlation of effective porosity and acoustic impedance attribute of seismic inversion, up scaled effective porosity logs as the initial data and acoustic impedance attribute of seismic inversion as secondary data have entered using Sequential Gaussian Simulation and Co-Kriging techniques. In order to simulate permeability, due to the good relationship between the effective porosity model obtained by simulating and up scaled permeability log, the effective porosity simulated as a secondary data and up scaled permeability log is used as initial data in Sequential Gaussian Simulation and Co-Kriging techniques. The results of validation indicate the accuracy of the present study and the efficiency of the Sequential Gaussian Simulation method in effective porosity and permeability modeling in this reservoir.

In any mining project, specially when the ore reserve is relatively large and distributed in homogeneously, drilling new boreholes to reduce the uncertainty associated with the grade estimation as well as reserve classification becomes unavoidable. However, selecting the optimum location of the new borehole, in terms of providing the maximum amount of information possible is a challenge. In this regard, customarily, the geostatistical approaches, based on minimizing the estimation error (variance) of blocks grade is used. However, in this method, the estimation variance of each block is independent of the sample grade values and only takes into account the spatial distribution of sample's location. This will ignore the local variability of sample values. Another approach to determine the optimum location for the new drilling boreholes could be based on the geostatistical simulation techniques. In simulation methods, the spatial variability due to the sample location, as well as the local variability associated with the data values are taken into consideration. In this paper, the Fractal Conditional Simulation Method (FCSM) is used to simulate the iron grades of anomaly No.3 of Gol-E-Gohar iron ore mine. The simulated data, then, were used to estimate the iron ore reserve and to perform a risk analysis. This method simulates the data sets, while the fractal co-dimension as well as the statistical and geostatistical parameters are kept unchanged. Finally, using uncertainty index and GET (Grade, Error and Thickness) function, the optimum location for new boreholes in the northern part of the ore-body was suggested.