GDWS-AGD Case Study
\subsection{Optimization Approaches}
The DoE is a systematic statistical tool that creates a proper set of experiments to identify the most sensitive factors that impact the response (sensitivity analysis). Furthermore, DoE is used in optimization and uncertainty assessment to obtain the most-likely scenario that achieves the optimal solution through a process and to quantify the risk analysis \citep{L06,ADR09}. Since the designed experiments are typically faster, cheaper and more flexible than physical or lab experiments, it is necessary to attain the most accurate model that mimics the physical model or process. To achieve that, the required numbers of parameters and interactions should be analyzed to make the interpretation and application …show more content…
Table ~\ref{table:tab3} illustrates the levels of the operational parameters in the advance GDWS-AGD case, the ranges of each parameter (minimum and maximum levels) in the optimization process, and the corresponding oil recovery factor, calculated after 10 years prediction period. The levels of each parameter in Table ~\ref{table:tab3} were combined by the LHS to produce multiple simulation runs, which were evaluated by the compositional reservoir simulator to calculate the oil recovery factor by the end of 10 years prediction …show more content…
Additionally, the optimal oil recovery was achieved at the high levels of the maximum bottom hole injection pressure, maximum oil rate, and minimum bottom hole pressure in sink wells as well as the low levels of the maximum bottom hole injection pressure, maximum water sink rate, and minimum bottom hole pressure in oil
The DoE is a systematic statistical tool that creates a proper set of experiments to identify the most sensitive factors that impact the response (sensitivity analysis). Furthermore, DoE is used in optimization and uncertainty assessment to obtain the most-likely scenario that achieves the optimal solution through a process and to quantify the risk analysis \citep{L06,ADR09}. Since the designed experiments are typically faster, cheaper and more flexible than physical or lab experiments, it is necessary to attain the most accurate model that mimics the physical model or process. To achieve that, the required numbers of parameters and interactions should be analyzed to make the interpretation and application …show more content…
Table ~\ref{table:tab3} illustrates the levels of the operational parameters in the advance GDWS-AGD case, the ranges of each parameter (minimum and maximum levels) in the optimization process, and the corresponding oil recovery factor, calculated after 10 years prediction period. The levels of each parameter in Table ~\ref{table:tab3} were combined by the LHS to produce multiple simulation runs, which were evaluated by the compositional reservoir simulator to calculate the oil recovery factor by the end of 10 years prediction …show more content…
Additionally, the optimal oil recovery was achieved at the high levels of the maximum bottom hole injection pressure, maximum oil rate, and minimum bottom hole pressure in sink wells as well as the low levels of the maximum bottom hole injection pressure, maximum water sink rate, and minimum bottom hole pressure in oil