جستجوی مقالات مرتبط با کلیدواژه "مونت کارلو" در نشریات گروه "زمین شناسی"

Gamma ray interactions with materials is a non-destructive method, which can be used for obtaining useful information such as the densities of the materials. In this work, the Monte Carlo Neutron – Photon (MCNP) code was used to simulate a block composed of sedimentary rocks with four holes (wells) so that each hole contained a detector. Then, by placing a Cs137 source in one of the holes and adding various percentages of water and oil to the sedimentary constituents of the block, the contrast value for the output and scattered gamma rays was obtained by the response function of the detector inside each of the wells. The response function of the detector in front of the source is greater than that of the other detectors. The obtained contrast in the block containing sedimentary rocks with forty percent of oil in the first to fourth holes is -219.10, -354.85, -174.75 and -197.30, respectively, and the contrast for the case of water in the first to fourth holes is -198.31, -330.87, -167.73 and -185026, respectively. Given the fact that by digging several holes either for the scattered or crossed gamma, one can obtain a better location for the detector position. It is also clear from the calculated valuesthat the contrast obtained from the second hole (that is in front of the source) is greater than the rest of the contrasts. However, due to the quantity D, the first hole is superior to the rest, because it can be distinguished from minerals by the percentage of materialsin the soil.

In the field of nuclear physics, the use of gamma rays is an important and practical method. This method is used in various industries such as oil and gas production, mining and quarrying, environmental monitoring and so on. The technology of using gamma rays is based on the interaction of the beams with materials. The three main phenomena are the interaction of gamma rays with photoelectric material, Compton scattering, and pair- production phenomena. Due to its very high sensitivity, activation analysis has become an important and applicable tool in various fields ranging from science and engineering to industry, mineral exploration, medicine, etc. One of the biggest advantages of analyzing by this activation method is that it can detect most isotopes with very high sensitivity. In this research, different simulations have been carried out on sedimentary rocks in order to find a suitable method for exploration of oil reservoirs. These simulations are performed in three different states and by comparing the results obtained in each case, the best results are selected to explore the oil reservoirs. 

Methodology and Approaches:

The Monte Carlo method is a class of computational algorithms that rely on random iterative sampling to calculate their results. To use this method, a structured input file must be provided, including problem information such as geometry, material type, source, output type, and so on. The code, based on this method, solves the problem using input file information and the crosssection library and generates the results in an output file. In the simulations by this code, a cubic block of about 2 meters is considered to be a type of sedimentary rocks, oil, gas, water and a combination of these rocks with water, gas or oil. The simulated cavity has a depth of 180 cm and a radius of 7.62 cm. A 2-inch NaI (Tl) detector is inserted into the specified hole.

According to the obtained results, it is possible to detect and identify the oil reservoirs and to separate the oil reservoirs from the gas and water reservoirs. The advantage of using this method is that due to the minimal amount of energy required for carbon interactions of 18.7 MeV, there is no need to investigate existing sources in nature, since the maximum energy emitted by these sources is less than 18.7 MeV. Thus, the sources in nature do not cause any disruption in the measurements.

One of the most sensitive and important issues in some civil engineering projects is slope design and application. The process of slope design always involve many uncertainties. Hence, it is impossible to accurately comment on its stability or instability. Most of the uncertainties in the slope stability analysis are related to the nature of materials, geometry, environmental conditions, model errors, and measuring errors as well. Therefore, the slope stability analysis with a deterministic approach which uses the concept of safety factor would often not result satisfactory. Consequently, the use of probabilistic methods is more advised. Accordingly, in recent years, the probability analysis has been used to slope stability analysis. In these analyses, the effective quantities of slope stability are considered as statistical distributions, and the reliability coefficient would then be a statistical distribution. Likewise, one of the approaches to simulate uncertainties in the probabilistic analysis is to use the variation coefficient. If the variation coefficient changes, the probability of failure will change accordingly. When the variation coefficient becomes a larger number, costly solutions are required to reduce the probability of failure. If the variation coefficient becomes low, the reliability will be increased and the required costs to reduce the probability of failure will be decreased. Therefore, determining the amount of variation coefficient in these analyses is very important. Furthermore, the correlation coefficient between the quantities is another effective parameter in computing the probability of failure.

In this research, the stability analysis of the slope facing the spillway of the Shiraz Kavar dam has been done in two probabilistic and deterministic methods. Since circular slip probability is more likely than other types of failure, in the analysis of the stability of this slope, the problem of circular failure is very important, and an appropriate equilibrium program should be used for circular failure analysis. Therefore, SLIDE software was used to slope stability analysis. For material behavior, the Hook-Brown failure criterion was applied. In order to determine the strength parameters of the criterion, Geological Strength Index (GSI), uniaxial compressive strength (UCS) and rock constant parameter mi were used. For crushed rock with a moderate quality of crushing, the GSI quality of the rock mass was about 23 to 38, which the average value of that for the rock mass of the overflow was assumed 35. Also, the uniaxial compressive strength of the rock was evaluated about 50 to 100 MPa with an average value of 75 MPa. In addition, the value of mi was 10, and due to mechanized drilling, the disturbance factor was considered to be 1. The amount of unit weight was assumed to be 22 kN/m3. The initial model used for deterministic and probabilistic analyses, is the Morgenstern-Price model. To conduct probabilistic analyses, Monte Carlo simulation was performed using random sampling method (RS-MC) and 200,000 sampling were used to converge the simulation results. To determine the coefficient of variation and the probability distribution of UCS, GSI and mi, the proposed values ​​of Hook (1998) were applied and for unit weight (γ) James Rodriguez and Sitar (2007) studies were used. Also, the minimum and maximum values ​​of UCS and GSI are determined based on the results of experiments, and Third Sigma rule was utilized for mi and γ quantities. Since the earthquake phenomenon is rarely of great intensity and the number of small earthquakes is higher, therefore the truncated exponential distribution function can be in good agreement with the results of the earthquake. Usually, the maximum magnitude of the earthquake acceleration coefficient is twice that of the average.

In the presented paper evaluation denotes that the safety factor computed by probabilistic analysis is given as a distribution function. The function provides a clearer view of failure condition. However, a deterministic analysis only illustrates a certain value for the failure. In addition, the results of the probabilistic analysis show that it is possible to optimize the dip of the slope; such that it remains completely stable and the volume of earthwork is also minimized. Therefore, by using probabilistic analysis, the optimal dip of the slope was determined. In these circumstances, the amount of earthwork was decreased by 28,000 cubic meters. Also, the sensitivity analysis of the variation coefficient and correlation coefficient between parameters are analyzed. The results of the sensitivity analysis of the failure probability versus the variation coefficient of the quantities showed that the quantities of sensitivity factor for static conditions is greater than the corresponding pseudo-static, and the GSI amount is the highest, while the specific gravity has the least effect on the probability value. In addition, the analysis indicated that if the GSI coefficient of over 21% is selected, the probability of a static failure is higher than the permissible limit. Also, increasing the variation coefficient of quantities by as much as 50% exhibits that the probability of static failure is still below the permissible limit. Also, the correlation coefficient between UCS and GSI shows that the higher variation coefficient of the quantities is chosen, the more variations of failure probability compared to . In the case of pseudo-static conditions, variations in the failure probability are linear in relation to , while in static conditions, these changes are exponential for an increase of 50% in the variation coefficient. Also, to reduce the coefficient of variation by 50%, the probability of static failure for different values of  is approximately zero.

Todays, sediment fingerprinting have been increasingly used to determine source contributions of sediments, but there is a need to continue refining procedure especially with respect to uncertainty analysis during modeling. In this research, we applied a Monte Carlo fingerprinting framework to exploring of uncertainty associate to source contributions of sand dune sediments in the Jazmurian region, Kerman province. In order to, after selecting optimum composite fingerprints using a two-stage statistical procedure including Kruskal-Wallis H test and discriminant function analysis, Collins mixing model was solved 10000 times in the MATLAB with 95% confidence levels (percentiles 2.5% and 97.5%). Among of 10 tracers, four tracers including Cr, Ni, Li and Co were selected as optimum composite fingerprints and then they entered as modeling procedure. Full uncertainty (0-100%) calculated for some sources especially Qs and Qal, and also for some sediment samples, Qt recognized as main source. Variability on the source for sand dune sediment samples is due to winds with multiple directions. The effectiveness of this technique to sand dune sediment fingerprinting suggests that they will be useful in other desert regions with active dune fields for recognition of their sources, quantifying source contributions and studying uncertainty of source contributions.

Porous media such as groundwater aquifers, oil and gas reservoirs have a large amount of heterogeneity. Modeling of flow and transport in such environments is based on a survey of the properties of porous media. In this paper, fractal permeability of porous media has been estimated using the Monte Carlo simulation. Using the results of previous studies and measurements on several sandstone samples, such as porosity, fractal dimension, fractal property and intrinsic permeability, using a Monte Carlo probabilistic model and a set of random numbers between 0 and 1 and pore diameter measurement, the sandstone permeability was determined. Finally the simulated and measured values of samples sandstone permeability were compared. Monte Carlo simulation results show that without any need for empirical coefficients, domain discretization with only a few laboratories measured parameters one can reasonably determine the fractal permeability of porous media. Measured and calculated permeability correlations are matched 83 percent.

One of the most important effects of the Manjil earthquake (M=7/7, 20/6/1990) has been triggering of the numerous landslides in the area affected by the earthquake. To predict landslides locations during the future earthquakes, it is essential to analyze the distribution of landslides triggered by the Manjil earthquake. In this paper, by employing the Monte Carlo Simulation, an area with 309 km2 including Chahar – Mahal and Chalkasar rectangles (near the Manjil Epicentre) was investigated in order to achieve a probabilistic model to predict earthquake-induced landslides. There is a good correlation between the provided map and the inventory map of landslides occurred during the Manjil earthquake.

Locating the critical slip surface and the associated minimum factor of safety are two complementary parts in a slope stability analysis. A large number of computer programs exist to solve slope stability problems. Most of these programs, however, have used inefficient and unreliable search procedures to locate the global minimum factor of safety. This paper presents an efficient and reliable method to determine the global minimum factor of safety coupled with a modified version of the Monte Carlo technique. Examples are presented to illustrate the reliability of the proposed method.