Spatial Distribution of Fractures in the Asmari Formation of Iran in Subsurface Environment: Effect of Lithology and Petrophysical Properties

The distribution of fractures and its dependence on lithology and petrophysical properties of rock in the Asmari Formation were examined using three wells data of one of the largest oil fields of southwestern Iran. Fractures were measured on cut cores. Mineral content and petrophysical data were obtained through thin section study and core plug measurement respectively. Influence of mineral composition and petrophysical property of rocks on fracture density was explored statistically. Increasing quartz (sand) and anhydrite content of rocks decrease and dolomite increases the threshold of fracture densities, however no significant relation was observed between calcite content of rock and fracture density. Increasing porosity and permeability of rock decrease the threshold of fracture density in some of the defined lithology groups. There are significant differences between the lithology groups in terms of fracture density, although the results in the three wells are not the same. In whole data, the highest fracture density can be observed in dolostone. Limestone and impure carbonates hold broader spaced fractures and sandstones display the least fracture density. The average fracture densities in the wells are strictly different. These differences are the result of the structural position of the wells and also the trend of the well and fractures. The distribution of fractures in most lithology groups can be explained by the function: , where F is relative frequency, D is fracture density and a, b, and c are constants.     

Value of Information Introduced by a Resource Model Updating Framework

A primary concern of the mining industry is to meet production targets, which are required and defined by customers. Deviations from these targets, in terms of quality and quantity, highly affect the economical aspect. Recently, an efficient resource model updating framework concept has been proposed aiming for the improvement of raw material quality control and process efficiency in any type of mining operation. The concept integrates online sensor measurements, obtained during production, into the resource model. In this way, due to the spatial variability, quality attributes of the blocks that will be produced in the next days or weeks are being updated based on real-time measurements. The concept has been applied in a lignite field with the aim of identifying local impurities in a lignite seam and to improve the prediction of coal quality attributes in neighbouring blocks. This paper investigates the added value of using the resource model updating framework by using the value of information analysis. The expected benefit of additional information (integration of the online sensor measurements into the resource model) is compared to a case where there is no additional information integrated into the process. These benefits are evaluated based on the economic impact determined by applying the resource model updating framework in mine planning.

     

Identifying Groundwater Potential in Crystalline Basement Rocks Using Remote Sensing and Electromagnetic Sounding Techniques in Central Western Mozambique

Exploring for groundwater in crystalline rocks in semiarid areas is a challenge because of their complex hydrogeology and low potential yields. An integrated approach was applied in central western Mozambique, in an area covered by Precambrian crystalline basement rocks. The approach combined a digital elevation model (DEM), remote sensing, and a ground-based geophysical survey. The aim was to identify groundwater zones with high potential and to identify geological structures controlling that potential. Lineaments were extracted from the DEM that had been enhanced using an adaptive-tilt, multi-directional, shading technique and a non-filtering technique to characterize the regional fracture system. The shallowness and amount of stored groundwater in the fracture zones was assessed using vegetation indices derived from Landsat 8 OLI images. Then, 14 transient electromagnetic (TEM) survey profiles were taken in different geological settings across continuous lineaments that were considered to be aligned along inferred faults. In the central lineament zones, the TEM soundings gave resistivity values of less than 300 Ωm at a depth of 20–80 m. The values varied with location. Conversely, values greater than 400 Ωm were observed at the sites away from the central zones. This contrast is probably caused by the differences in permeability and degree of weathering along the fractured zones. These differences could be key factors in determining groundwater occurrence. By integrating five water-related factors (lineament density, slope, geology, vegetation index, and proximity to lineaments), high groundwater potential zones were located in the vicinity of the lineaments. In these zones, vegetation remains active regardless of the season.     

基于地理信息系统的植被退化风险评估:以伊朗Qareh Aghaj盆地为例（英文）

伊朗南部面临各种类型的土地退化问题,其中植被退化是其中主要类型之一。本研究选择蒙特河上游地区的Qareh Aghaj盆地(1 265 000公顷)进行土地退化的风险评估。基于多源的植被退化指标的数据,开发了一种新的模型来评估植被退化的风险。该模型选择植被退化的九个指标,确定了具有"潜在风险"(风险区)和"实际风险"区域的领域,并预测未来恶化的可能性。基于对这些指标的GIS分析、编制风险地图将有助于确定这些地区的优先次序以启动补救措施。通过定义九个指标严重程度等级的阈值,首先在GIS中制备了每个指标的风险地图。风险等级是根据风险评分确定的,通过给指标分配适当的属性,风险图是在GIS中通过叠加9个风险地图来综合生成的,结果发现实际风险区域在该流域范围内(78%)广泛存在。     

New climatic zones in Iran: a comparative study of different empirical methods and clustering technique

Theoretical and Applied Climatology - Recently in agricultural and industrial sectors, researchers have started to classify the climate of a region using empirical methods and clustering. This...     

The assessment of rock slope instability along the Khosh-Yeylagh Main Road (Iran) using a systems approach

The slope instability is connected to a large diversity of causative and triggering factors, ranging from inherent geological structure to the environmental conditions. Thus, assessment and prediction of slope failure hazard is a difficult and complex multi-parametric problem. In contrast to the analytic approaches, the systems approaches are able to consider infinite number of affecting parameters and assess the interactions of each couple of the parameters in the system. This paper presents a complete application of the rock engineering systems approach in prediction of the instability potential of rock slopes in 15 stations along a 20?km section of the Khosh-Yeylagh Main Road, Iran as the case study of the research. In this research, the main objective has been defining the principal causative and triggering factors responsible for the manifestation of slope instability phenomena, quantify their interactions, obtain their weighted coefficients, and calculate the slope instability index, which refers to the inherent potential instability of each slope of the examined region. The final results have been mapped to highlight the rock slopes susceptible to instability. Finally, as a preliminary validation on the utilization of systems approach in the study region, the stability of investigated rock slopes were analyzed using an empirical method and the results were compared. The comparisons showed a rather good coincidence between the given classes of two methods.     

Gravity and magnetotelluric investigations to elicit the origin of Hammam Faraun hot spring,Sinai Peninsula,Egypt

There is a clear correlation between the principal areas of current geothermal development and the seismically active boundaries of the moving segments of lithosphere defined by the plate tectonic models of the Earth. The tectonic position of Egypt in the northeastern corner of African continent suggests that the most important areas for geothermal exploration are in the region where a cluster of hot springs with varied temperatures was located around the Gulf of Suez. Gravity and magnetotelluric surveys were made in the area of Hammam Faraun hot spring, which represents the most promising area for geothermal development in Egypt. These surveys were carried out for the purpose of eliciting the origin of Hammam Faraun hot spring. The results of the analyses and interpretations of these data show that the heat source of the hot spring is due to uplift of hot basement rock. This uplift may cause deep circulation and heating of the undergroundwater.     

Burden prediction in blasting operation using rock geomechanical properties

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).