The efficiency of the SAGD process depends on two important factors: reservoir properties and operating conditions. SAGD performance was investigated based on the variables of reservoir properties such as thickness, porosity, permeability, oil saturation, viscosity, rock thermal conductivity, along with operating variables as including preheating, injector/producer spacing, injection pressure, steam injection rate and subcool temperature. In addition, the economic risks associate to the high capital and operation expenditures, and uncertainties of oil and gas prices in the market. In order to manage the uncertainties of oilsands project, we need the quantitative analysis of concerned parameters affecting returns. Then, we can propose optimization design for operating conditions.The previous studies conducted sensitivity analysis and optimization of SAGD performance by classical methods. Therefore, there was a lack of confidence level because they did not determine the significance level of parameters and ignored interactions effects between considered parameters, lead to low efficiency issues in a field operation. Furthermore, the economic models were not comprehensive enough with limited consideration on few factors. These restrictions of classical method can be avoided by applying D-optimal design and response surface methodology to find the best regression model for SAGD performance.There were a total of 75 cases for screening reservoir and operational parameters with the NPV responses based on the D-optimal design. The results showed that reservoir properties have a greatest influence on the SAGD performance with ranking order of porosity, thickness, oil saturation, permeability, viscosity, respectively. The optimization design of operating conditions obtained the maximum NPV when vertical well spacing 9m, injection pressure 5,000kPa.

This study presents a procedure for calculating the change of the safety factor for unsaturated slopes of homogenous, residual soils suffering from rainfall infiltration within Khanh Vinh district, Khanh Hòa province. Rainfall is supposed as a main trigger caused failure of the potential sliding slopes. Rainwater into the slope due to infiltration caused an increase in moisture content and negative pore water pressure; a decrease in matric suction and in shear strength on the failure surface. Thus, slopes are reduced stability and can be failed. Soil permeability and rainfall intensity were found to be the primary factors controlling the instability of slopes due to rainfall, while the initial water table location and slope geometry only played a secondary role. A numerical model of analysis coupled seepage-stability used to simulate the seepage and slope stability under conditions of specific environment such as soil permeability, rainfall intensity, water table location and slope geometry in the study area. The relationships between safety factor and rainfall intensity, soil permeability, angle slope, high slope were identified to provide a good indication for the management of landslide hazards under the effects of rainfall.

The purpose of this study is to produce landslide hazard map in Khanh Vinh district, Khanh Hoa province using logistic regression method integrated with GIS analytical tools. The spatial relationship between landslide-related factors such as topography; lithology; vegetation; maximum precipitation in year; distance from roads; distance from drainages; distance from faults and the distribution of landslides were used in the landslide hazard analyses. Using success rate and prediction rate curve assess the fit and accuracy of logistic regression method. The results show that this method have the goodness of fit and the high accuracy (Areas Under Curves - AUC = 0.8 ~ 0.9). Bayesian Model Average (BMA) of the R statistical software was applied to identify the most influential factors and the combinatorial optimization models of landslide-related factors. There are four the most important landslide-related factors and five combinatorial optimization models of landslide-related factors. Model 3 (slope angle, slope aspect, altitude, distance from roads and maximum precipitation in year) is the best optimization.

Sand production in Sandstone reservoirs is a complex problem to Oil & Gas companies. Many methods have used to solve this problem but these methods only have effect for the first period of production without effective in long time. Sand production causes big damages such as: producing tools corrosion in hole, separating tools corrosion at surface, formation collapse, so sand production in well is always the urgent problem. Based on formation characteristic research, advantages and disadvantages of sand failure analysis and combining the advanced methods, this study introduces Production Sand Pressure Model to predict sand productivity in sedimentary reservoirs in field X in Cuu Long basin.