排序方式： 共有3条查询结果，搜索用时 15 毫秒
1
1.
Dehoo manganese deposit is located 52 km to the south of Zahedan in Sistan and Baluchestan Province, southeastern Iran. This deposit that lies in the central part of the Iranian Flysch Zone is lenticular in shape and lies above the micritic limestone-radiolarite cherts of the upper Cretaceous ophiolite unit. It is hosted within the reddish to brown radiolarite cherts and in places interlinks with them, so that the radiolarite chert packages play a key role for Mn mineralization in the region. Investigated ore-paragenetic successions and the geochemical characteristics of the Dehoo deposit were studied by means of major oxide, trace, and rare earth element (REE) contents that provide information as to the mineral origin. Strong positive correlations were found between major oxides and trace elements (Al2O3-TiO2, r = 0.95; TiO2-MgO, r = 0.94; Fe2O3-Al2O3, r = 0.90; MgO-Al2O3, r = 0.84; MgO-Fe2O3, r = 0.88; Fe2O3-TiO2, r = 0.91; Fe2O3-K2O, r = 0.74; Al2O3-K2O, r = 0.69; Al2O3-V, r = 0.72; TiO2-V, r = 0.73, and MgO-V, r = 0.69) that testify to the contribution of mafic terrigenous detrital material to the deposit. Chondrite-normalized REE patterns of all ore samples are characterized by negative Ce (0.06–0.15, average 0.10) and slightly positive Eu (0.29–0.45, average 0.36) anomalies. Based on ratios of Mn/Fe (average 56.23), Co/Ni (average 0.33), Co/Zn (average 0.38), U/Th (average 3.40), La/Ce (average 1.45), Lan/Ndn (average 2.16), Dyn/Ybn (average 0.33), and light REE/heavy REE (average 8.40; LREE > HREE), as well as Ba (average 920 ppm) and total REE contents (average 6.96 ppm) negative Ce and positive Eu anomalies, Dehoo could be considered a predominantly submarine hydrothermal Mn deposit complemented by terrigenous detrital mafic material. 相似文献
2.
Saeid Sadeghnejad Mohsen Masihi Mahmoudreza Pishvaie Peter R. King 《Mathematical Geosciences》2013,45(3):321-340
Complicated sedimentary processes control the spatial distribution of geological heterogeneities. This serves to make the nature of the fluid flow in the hydrocarbon reservoirs immensely complex. Proper modeling of these heterogeneities and evaluation of their connectivity are crucial and affects all aspects of fluid flow. Since the natural variability of heterogeneity occurs in a myriad of length scales, accurate modeling of the rock type connectivity requires a very fine scheme, which is computationally very expensive. Hence, this makes other alternative methods such as the percolation approach attractive and necessary. The percolation approach considers the hypothesis that a reservoir can be split into either permeable (sand/fracture) or impermeable rocks (shale/matrix). In this approach, the connectivity of the permeable fraction governs the flow. This method links the global properties of the system to the density of the permeable objects distributed randomly in the system. Moreover, this approach reduces many results to some simple master curves from which all-possible outcomes can be predicted by simple algebraic transformations. The current study contributes to extending the applicability of the methodology to anisotropic systems as well as using the complicated and more realistic sandbody shapes (for example, ellipsoids). This enables us to attain a better assessment of the connectivity and its associated uncertainty of the complicated rock types. Furthermore, to validate the approach, the Burgan reservoir dataset of the Norouz offshore oil field in the south of Iran was used. The findings are in conformity with the percolation approach predictions. 相似文献
3.
Saeid Jamshidi Ramin Bozorgmehry Boozarjomehry Mahmoud Reza Pishvaie 《Advances in water resources》2009
In pore network modeling, the void space of a rock sample is represented at the microscopic scale by a network of pores connected by throats. Construction of a reasonable representation of the geometry and topology of the pore space will lead to a reliable prediction of the properties of porous media. Recently, the theory of multi-cellular growth (or L-systems) has been used as a flexible tool for generation of pore network models which do not require any special information such as 2D SEM or 3D pore space images. In general, the networks generated by this method are irregular pore network models which are inherently closer to the complicated nature of the porous media rather than regular lattice networks. In this approach, the construction process is controlled only by the production rules that govern the development process of the network. In this study, genetic algorithm has been used to obtain the optimum values of the uncertain parameters of these production rules to build an appropriate irregular lattice network capable of the prediction of both static and hydraulic information of the target porous medium. 相似文献
1