Permeability is one of the most important parameters of reservoir rocks, it illustrates the capacity of rock to transmit fluids (oil, gas, water) in pore spaces. Permeability data can be obtained from routine core analysis in laboratory on 1.5 in plugs and sidewall core. However, coring is limited due to cost issue, so permeability prediction in uncored sections play a significantly important role. The variety of methods developed to estimate permeability using pore scale such as Kozeny-Carman, Swanson and Pittman. In fact, those equations are applied individually to estimate permeability. In this research, permeability estimation methods will be used on the same rock (sandstone or carbonate rock) to detect the suitable method for each rock type.
Porosity, permeability data from routine core analysis and pore throat size from mercury injection capillary pressure on sandstone and carbonate rocks from Cuu Long and Song Hong basins in Vietnam will be gathered and permeability estimation conducted by using Hydraulic Flow Unit (HFU), Mercury Injection Capillary Pressure (MICP) and Pittman methods based on that data. Estimated permeability obtained from each methods will be compared with core permeability, the method with the highest R-squared be selected.
The research shows that Hydraulic Flow Unit is the most suitable methods for permeability prediction on sandstone with R-squared > 0.9. On the other hand, mercury injection capillary pressure is the most accurate method to estimate permeability on carbonate rocks related to heterogeneity and complicated pore system. That results will help engineers have a fast and accurate decision for permeability prediction methods selection on sandstone and carbonate rocks.
In addition, the empirical equations were derived to predict permeability on sandstone and carbonate rocks with the highest coefficient of correlation in multiple regression analysis and based on the relationship between porosity, permeability and pore throat size.
Fair-trade in coffee production offers an opportunity to improve farmers' position in the market. The research has used a multinomial logit model with the MLE method to analysis the factors affecting the awareness probability about the fair-trade model of the coffee farmers Data were collected by directly interviewing 220 farmers in Xuan TruongCommune, Da Lat City, Lam Dong Province where the fair-trade model has been applied to coffee production at the Cau Dat coffee cooperatives. The results showed that the awareness probability of farmers about the fair-trade coffee model was 21,68% while there was only 0.12% of famers knowing this but not clear. In addition, factors affecting the awareness probability in the fair-trade coffee model are educational level, experience, communication, understanding of fair-trade, and coffee cultivation; of which communication and understanding of fair-trade positively influencing the farmers' awareness.
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