sequential gaussian simulation

An authentic understanding of formation pore and fracture pressures is essential to define a safe and optimum mud window in drilling oil and gas wells. This investigation is a challenge in formation pressure studies in the South Azadegan field, which is typically carbonated with very low traces of shale beds, except in the Kazhdumi Formation. The wells drilled in this field hinged on the geological information and pore pressure alterations, can be categorized into three classes; Gachsaran, Pabdeh, and surface formations containing marl. These parameters directly affect the selection of the casing shoe depth and the well schematic. Correspondingly, target reservoir formations, i.e., Sarvak, Kazhdumi, Gadvan, and Fahliyan, and well profiles are other parameters that can classify wells in terms of drilling. It is necessary to analyze and model all upper formation pressures to obtain more precise results to investigate the pore pressure profile in these formations. Effective pressure log data reveals an increasing trend in formation pressure with depth in all wells. Besides, there are a few effective pressure-velocity data pairs in the total data of the Fahliyan Formation of Azadegan wells. With the small number of effective pressure-velocity data pairs in the total data of the Fahliyan Formations of Azadegan wells and the very low correlation coefficient of the Bowers relation for the wells of the Fahliyan Formations, it was necessary to separate this formation into two upper and lower parts. So the modeling has been performed by constructing compressional velocity-effective pressure cubes. This research was based on the data gathered from various drilled wells in this region and the interpretation of seismic data. Also, the effective, pore and formation fracture models have been determined from the integrated geostatistical models validated with the pressure-volume fractal model. The most heightened correlation between the final velocity and effective pressure cubes corresponds to the Ilam with 0.71 and the lower Fahliyan Formations with 0.86, which signifies the model's accuracy with the original data. Based on the final pressure cubes of the formation, the maximum pore pressure of 10,000 psi in the Gadvan to Upper Fahliyan Formations and the maximum fracture pressure of 13,000 psi in the lower Fahliyan to Gotnia Formations have been obtained. In this research, an innovation has been made to study the formation pressures utilizing fractal pressure-volume (P-V) methods. Also, for the construction of the final formation pressure cube model in the entire area of the South Azadegan field, for the first time, the combination of geostatistical methods of sequential Gaussian simulation (SGS) and co-kriging with the acoustic impedance (AI) cube obtained from seismic inversion has been used together. Computation of the Logratio matrix resulting from the fractal pressure-volume model revealed the maximum overall accuracy (OA) in the dominant limestone intervals as 0.74 at the depths of 2000-3000 meters, corresponding to the Sarvak to Asmari Formations. The results exhibit the high correspondence of the pore pressure cube model, obtained by sequential Gaussian simulation (SGS) combined with co-kriging and acoustic impedance inversion...

Investments and progress of mining projects depend on the quantity and quality of mineral resources and reserves; therefore, it is significant to know the reliability of the ore deposit estimation based on various methods. This research has investigated the role of geostatistics and artificial neural networks in modeling, estimating the concentration of exploration blocks, and estimating mining reserves. Spatial distribution modeling of iron values has been performed using three methods of ordinary kriging, Gaussian simulation, and artificial neural networks. To construct the block model of ore deposit, 29 exploratory boreholes with an average depth of 142.75 meters and a total length of 4139.9 meters were used. After drawing the variograms, the search ellipse was calculated, and a 3D model was obtained to estimate the concentration by ordinary kriging and Gaussian sequential simulation methods. Also, modeling and estimating the concentration was done by the artificial neural network method. Results showed that the artificial neural network technique has high validity. Also, due to its ease of use and no need for extra variographic calculations can be an appropriate alternative to geostatistical and simulation approaches. Finally, based on different cut-offs, the concentration-volume curve was drawn. The results show that this ore body has 439 million tons of ore with an average concentration of 42 % per 20% cut-off concentration.

This research aims at grade modeling, reserve classification, and assessing risk in grade-tonnage curves in a case study pertaining to Iron mineralization. The Darreh-Ziarat Fe deposit, located in west of Iran, is selected for this research work. Statistical studies were performed on the results of the analysis of ten boreholes. The ordinary kriging (OK) method is run to design a 3D model of grade by incorporating the drilling results. The indicator kriging (IK) method was used to remove waste blocks from the ore block model. Reserve classification was carried out in two ways. A grade-tonnage curve was displayed for different cut-off grades in accordance with the estimation results. In the second section, all the previous steps were performed by the simulation method. For this purpose, the sequential indicator simulation (SIS) method was utilized to remove waste blocks. A grade estimation block model was also generated using sequential Gaussian simulation (SGS). One hundred iterations of the SGS were run to investigate the uncertainty of the grade estimation, create models of the probability of exceeding the specified cut-off grades, and generate an E-type model. The kriging grade-tonnage curve showed that with respect to the cut-off grade of 20%, the tonnage and the average grade of iron are equal to 2.9 million tons and 40.79%, respectively. Also, the E-type grade-tonnage curve showed that with respect to the cut-off grade of 20%, the tonnage and the average grade of iron are equal to 3.1 million tons and 40.92%, respectively. Finally, Iron mineralization targets were recognized by comparing the kriging and the E-type methods.

The study area is the Yeganlly and Qinranjeh area, southwest of Zarshuran gold mine, 35 km away from Takab city, West Azarbaijan province. The study area is located on the southwest edge of the mine. Only 17 borehole data were available for this study and were evaluated. Geophysical data includes 17 parallel profiles with a length of about 735 meters with a 30 meter distance electrode and a polar-bipolar mapping array according to the topography of the area. The objective of introducing and presenting a method for simulation and single-multivariate estimation using the most widely used methods, such as usual cracking, ordinary cokriging, sequential simulation, sequential coexistence simulation, in order to reproduce more precisely the initial region variables and Secondary will be. Geophysical data was constructed of a so-called sulfide factor. After normalizing, for each variable, the variography chart was drawn and estimated using the Kriging method according to the variogram in the borehole coordinates. Then, by normalizing the data in the specimen, analysis of the main components on the variables was performed using SPSS software. The output of this part was two factors that the fact that the amount of gold mineralization was high was introduced as a mineralization factor. Now, with two existing factors, namely, the sulfide factor (geophysical factor) and the gold mineralization factor in the studied area, the combined interactions between the built-in block models were combined with the estimation and simulation; ultimately, validation was also performed by evaluating and estimating two boreholes Isolated randomly using other specimens at the end. It was found that according to the evaluated results, when estimating the gold grade when using sulfide factors and mineralization in coking, the gold monorangle estimation by conventional Kriging estimation is closer to the real values of gold.

Mining operations and waste resulting from it can be considered as one of the most important sources of hazardous elements in the environment. Identification of the spatial distribution of toxic elements in the mine waste dump systems requires the assessment of environmental hazards and strategies. To achieve the goal, the sampling was conducted on a waste dump located in the east of Angouran mine at an area of 300,000 square meters. For this purpose, 38 soil samples were selected for study and modeling. Statistical studies of the data set for As, Cd, Cu and Co elements were performed. In addition, Q-Q plot was used to remove outlier from the data, which resulted in an increase in the correlation coefficient of the results. In the next step, statistical analyses were performed to determine variogram parameters for the studied elements and the distribution of toxic elements was modeled on the basis of sequential Gaussian simulation (SGS) method, which results in high concentrations As in the south, west, and north of the dump. Elevated concentration of Cd can be seen in the southwest and northeast parts of the dump. Also, high concentrations of Co and Cu accumulate in the western, southern and northern parts of the dump. In order to validate variograms, cross-validation method was employed which is a fundamental method for comparing the effect of different variogram models and simulation methods on interpolation results. Correlation coefficients for As, Cd, Cu and Co elements were 0.885, 0.8056, 0.6867 and 0.9792, which represent the validity of simulation results.

Since in exploratory works the number of collected data are much smaller than area of the study area therefore finding a strong tool for deposit spatial modelling with according to limited collected points is very important. According to study deposit characteristics different geostatistical simulation algorithms are presented with the ability to generate different models of deposits for the solution of this problem. The existence or absence of high skewness of exploratory collected data in study area is one of the reasons for using sequential indicator simulation method or sequential Gaussian simulation method. In this article according to the type of study activities of programing network graphical evaluation and review technique (GERT) for explorative projects management and creation a suitable model in use of sequential gaussian simulation and sequential indicator simulation were used. For implement the possible network activities used of geochemical data of Dali Cu-Au porphyry deposit. Comparison of SIS simulation results on gold data for high Skewness with results of SGS simulation on copper data for low skewness indicates correlation top of these two elements is the existence of Cu-Au porphyry resource along the NE-SW in in Dali area. The results adaptation of the survey method with geological evidence proved work steps.

Summary: Simulation method give use most probable models that could be to obtain the best and worst case of a resource. So, average of these models is similar to estimated model but will not have estimated model problems. In the introduced new method after determining the rock type probability models the grade distribution is evaluated using geostatistical simulations. This method in addition to containing the probability models use from simulated grades that case be most authentic. Performance of method presented using a case study of a porphyry copper mine in Chile. Geological modeling is one of essential processes in ore reserves and resources evaluation that is precedence over the grade estimate. It is containing the deterministic division of deposit into smaller subdomains that called geological units. Although it cased to present the better treatment from the spatial grade variability, but it is not possible to present the uncertainty models in the boundaries of geological units that are need to classify the ore reserves and resources according to international standards such as JORC. This article presents an approach that uses geological uncertainty that could be incorporate in the evaluation of mineral resources. Introduced method applied on the rihoblanco deposit case study in chile. Methodology and Approaches: At first to obtain probabilistic descriptions of three different lithological units the geostatistical simulation have been used in this deposit (granodiorit, tourmaline breccia and another breccias). Then to determine the grade uncertainty of the deposit the conditional simulations have been applied. Finally, mineral resource model presented using weighting the copper grade realizations by occurrence probability of deferent geological units. Presented model able to produce a grade model that contain the geologic models uncertainty with avoidance of artificial models that caused by deterministic boundary hypotheses between high grade and low grade areas of ore in near the geologic boundaries. Results and Conclusions: Introduced method presented better results compared to the kriging method. Results shown that evaluation of the deposit geological uncertainty prevented from creates the unrealistic models of kriging method. Reliability of model is evaluated which less standard deviation of weighted realizations conditional variance show that reliability of them are suitable. Also, coefficient of variation in domains boundary was low which shows the method is suitable and uncertainty in boundaries is low. Also, presented new method compared with general simulation of grade distribution that new method represented higher tonnage percentage in grade-tonnage curves.

Ore reserve amount and grade are critical variables in determining technical and economical indicators. These factors always associate with uncertainty. Therefore it is essential to assess risk along with them by using appropriate methods. One of these methods is conditional simulation that allows providing probability maps and determining orebody risk rate. Unlike the common geostatistical estimation methods which involve some sort of smoothing، conditional simulation provides a tool for quantifying risk rate along with grade and design and planning parameters. In this research sequential Gaussian simulation has been applied to assess the uncertainty associated with reserve grade and tonnage of Esfordi phosphate mine and prepared confidence interval and E-type maps. Finally، the rate and the effect of estimated risk have been assessed and analyzed. According to simulation result، ore reserve of Esfordi mine is between 12/212 and 18/835 tones and the average of ore amount is 15/269 with average grade of 16/8%. The results show 30% -risk with blocks.