Ali Akbar Rahimi Bahar
Abstract
Accurate estimation of hydrocarbon volume in a reservoir is important due to future development and investment on that reservoir. Estimation of Oil and Gas reservoirs continues from exploration to end of reservoir time life and is usual upstream engineer’s involvements. In this study we tried to make ...
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Accurate estimation of hydrocarbon volume in a reservoir is important due to future development and investment on that reservoir. Estimation of Oil and Gas reservoirs continues from exploration to end of reservoir time life and is usual upstream engineer’s involvements. In this study we tried to make reservoir properties models (porosity and water saturation) and estimate reservoir volume hydrocarbon based on artificial neural network tools, petrophysical and geophysical data. So with gridding the reserve, separate it to same volume cells. Based on porosity and lithology variation in wells, constructed petrophysical zonation in each well and by correlation these zones in wells reservoir has been zoned. Porosity, water saturation and 3D seismic data have been averaged in cells and assigned one value for each cell. At final a three layer perceptron neural network by back propagation error algorithm has been designed and trained by using cells which had petrophysical data; then these parameters have been estimated in other cells and original hydrocarbon in place calculated and compared with results from Mont Carlo method.
Amir Pakdel; Masoud Emami; Hassan Farhangi; Mohammad Habibi Parsa
Abstract
Al-SiC composites are among the most demanding metal matrix composites due to their excellent strength, good ductility, good corrosion resistance, low coefficient of thermal expansion and reasonable price. Manufacturing of cast metal matrix composites usually involves some problems such as inhomogeneous ...
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Al-SiC composites are among the most demanding metal matrix composites due to their excellent strength, good ductility, good corrosion resistance, low coefficient of thermal expansion and reasonable price. Manufacturing of cast metal matrix composites usually involves some problems such as inhomogeneous distribution of the particles due to poor wetability of ceramics to molten alloys, porosity and formation of particle-void clusters. Thus it seems necessary to utilize secondary processes for these materials in order to obtain suitable properties. In this research Al6061 composites reinforced with 10 volume percent SiC particles of 48?m average size were produced by the stir casting method and effect of extrusion process on the microstructure and strength of these materials was investigated. Results showed that the average particle size and porosity of the composite samples decreased after extrusion. Moreover, tensile strength of the composite increased by increasing the extrusion temperature and/or the extrusion ratio since the pores in the microstructure diminished and the interface bonding was improved.
