#TITLE_ALTERNATIVE#

• Main Author: BAYU PRATAMA (NIM : 12214020), ADITYA
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/25017
• Institution: Institut Teknologi Bandung

Cyclic Steam Stimulation is one of the most common enhance oil recovery (EOR) method for heavy oil reservoirs. The cyclic steam stimulation design including several methods including analytical, numerical, and statistical approach. The better design in this EOR method is necessary to reach the better production profile. The aim of this study is to develop the best design scenario for CSS method that applies generally in heavy oil reservoir. <br /> <br /> <br /> The generation of reservoir model is based on SPE comparative solution project with few adjustments in geological properties for it’s generality. Analytical approach from Gomaa’s correlation is used in this study to know the best condition possible for the usage of the analytical method. From numerical approach, controllable parameters is designed and sensitivity study was conducted on each parameter to find the correlation with production profile. At the end, with the help of mechanism analysis, combined parameters are optimized to find the best design possible to achieve highest cumulative production. <br /> <br /> <br /> The results shows that Gomaa’s correlation is accurate and relatively reliable when the steam zone heated region is defined as the region that experiencing 1 degree fahrenheit temperature rise. And the combined parameter optimization shows that the best scenario is for the same injection volume, higher injection rate in shorter injection period is preferable and for further optimization, for higher injection rate the longer soak time period is necessary. Finally with the oil-steam ratio graph, cumulative production can be forecasted for any injection rate and injection time feasible with operation constraints. <br /> <br /> <br /> The novelty of this study is it’s comprehensive analysis and it’s generality. This study also provides new production forecast method and best design possible for combined controllable parameter with the intercorrelation response of each other whereas other studies mainly recommend the best scenario for each controllable parameter. This can resulting the best and the most feasible outcome with limitations in field operations.