Exploration
H. Sabeti; F. Moradpouri
Abstract
The geo-statistical simulation algorithms for continuous spatial variables have been used widely in order to generate the statistically-honored models. There are two main algorithms doing the continuous variable simulation, Sequential Gaussian Simulation (SGS) and Direct Sequential Simulation (DSS). ...
Read More
The geo-statistical simulation algorithms for continuous spatial variables have been used widely in order to generate the statistically-honored models. There are two main algorithms doing the continuous variable simulation, Sequential Gaussian Simulation (SGS) and Direct Sequential Simulation (DSS). The main advantage of the DSS algorithm against the SGS algorithm is that in the DSS algorithm no Gaussian transformation of the original data is made. In this work, these two simulation algorithms are explained, and their applications to a 3D spatial dataset are deeply investigated. The dataset consists of the porosity values of 16 vertical wells extracted from an actual cube obtained by a seismic inversion process. One well data is excluded from the simulation process for the blind well test. Comparison between the histograms show that the histogram reproduction is slightly better for the SGS algorithm, although the population reproductions are the same for both SGS and DSS results. The DSS algorithm reproduce the mean of input data closer to the mean of well data compared to that of the SGS algorithm. Considering one realization from each simulation algorithm, the RMS error corresponding to all simulated cells against the real values is approximately equal for both algorithms. On the other hand, the error show a slightly less value when the mean of 100 realizations of the DSS result is considered.
Environment
S. Abbaszade; F. Mohammad Torab; A. Alikhani; H. Molayemat
Abstract
In geochemical exploration, there are various techniques such as univariate and multivariate statistical methods available for recognition of anomalous areas. Univariate techniques are usually utilized to estimate the threshold value, which is the smallest quantity among the values representing the anomalous ...
Read More
In geochemical exploration, there are various techniques such as univariate and multivariate statistical methods available for recognition of anomalous areas. Univariate techniques are usually utilized to estimate the threshold value, which is the smallest quantity among the values representing the anomalous areas. In this work, a combination of the Sequential Gaussian Simulation (SGS) and Gap Statistics (GS) methods was utilized as a new technique to estimate the threshold and to visualize the anomalous regions in the Hararan area, which is located in SE Iran, and consists of copper mineralization that seems to be connected to a porphyry Cu-Mo system. Furthermore, the most important advantage of this method is the reliable assessment of the anomalous areas. In other words, the anomalous areas were discriminated in terms of their probability values. The regions with high probability values were reliable and appropriate to locate the drilling points for a detailed exploration. It not only decreases the risk, cost, and time of exploration but also increases the drilling point reliability and precision of reserve estimation after drilling. In this research work, the results of analysis of 607 lithogeochemical samples for the element Cu were used. The SGS method was performed on the transformed data and 50 realizations were obtained. In the next step, the back-transformed realizations were utilized to obtain an E-type map, which was the average of 50 realizations. Moreover, the results of the GS method showed that the Cu threshold value was 228 ppm in the area. Therefore, using the E-type map, areas with values greater than 228 ppm were introduced as the anomalous areas. Finally, the probability map of the exceeding threshold values was acquired, and the anomalous districts located in the southern part of the studied area were considered as more reliable regions for future detailed exploration and drilling.
Exploitation
S. Talesh Hosseini; O. Asghari; Seyed R. Ghavami Riabi
Abstract
Due to the existence of a constant sum of constraints, the geochemical data is presented as the compositional data that has a closed number system. A closed number system is a dataset that includes several variables. The summation value of variables is constant, being equal to one. By calculating the ...
Read More
Due to the existence of a constant sum of constraints, the geochemical data is presented as the compositional data that has a closed number system. A closed number system is a dataset that includes several variables. The summation value of variables is constant, being equal to one. By calculating the correlation coefficient of a closed number system and comparing it with an open number system, one can see an increase in the values of the closed number system, which is false. Such features of this data prevent the application of standard statistical techniques to process the data. Therefore, several methods have been proposed for transforming the data from closed to open number systems. There are various geostatistical methods consisting of estimation and simulation methods in order to model a deposit. Geostatistical simulations can produce various models for a deposit with different probability percentages. The most applicable geostatistical simulation method is the sequential Gaussian simulation technique, which is highly flexible. In this work, 392 Litho-geochemical data of the Baghqloom region of Kerman in Iran consisting of 20 elements were at first converted using an open number system. Afterwards, the elements that were helpful for exploring the area and were normally standard were simulated for 100 times. After the simulations, the valid output was chosen using geostatistical validation. The maps derived from the simulations revealed the enriched concentrations of mineralization elements in the central regions.
