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In order to determine the most influential geological parameters affecting the Gas Assisted Gravity Drainage process, sensitivity analysis was conducted through Design of experiments (DoE). DoE combines multi-level of each parameter to create many computer experiments evaluated by the compositional reservoir simulation. The factors that were adopted in this case are horizontal permeability, anisotropy ratio $K_{v}/K_{h}$, and porosity, all given for the entire reservoir. The most influential factors are diagnosed through building a statistical linear modeling, partial t-test, and Analysis of Variance (ANOVA).
Firstly, more than 80 computer experiments (simulation job) were created by the Latin Hypercube Sampling approach. The simulation
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If a variable with horizontal straight line, it has no effect on the response and should be removed form the linear model, specifically the porosity variable. Based on the full linear model and as shown in Figure ~\ref{fig:sa1lm}, the reservoir porosity factor has no effect on the flow response as it has horizontal line of residual. Figure ~\ref{fig:sa1_lm} shows the basic diagnostic plots for the full linear model. The residuals vs. fitted plot is used to determine if the linear relationship between the predictor variables and outcome variables are captured by the model. An accurate model will have a residuals vs. fitted plot with a horizontal line with residuals above and below it without forming a patterns or groupings, as seen in the top left plot. The normal Q-Q plot, shown in the top right plot, is used to determine if the residuals are normally distributed. A good normal distribution will have a normal Q-Q plot which follows a straight line, as do our results. The Scale-Location plot shows if the residuals are spread equally along all ranges of the predictors. Ideally, the residuals should be randomly spread across all fitted values and not trend to one area, as ours do in the bottom left plot. Lastly, the Residuals vs Leverage plot determines how big of an impact outliers points have on the regression curve. Outliers away from the Cook’s distance will have a large impact, while those nearby will not. The bottom right plot shows that all but 3 residuals are

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