ESTIMATING SOLUTION GAS-DRIVE FUTURE WELL PERFORMANCE AS A FUNCTION OF OIL MOBILITY PROFILE

• Main Author: Suyoto, Inge
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/70030
• Institution: Institut Teknologi Bandung

Wiggins et al. stated that the main parameter to know a solution gas-drive performance lies in its mobility function. In predicting a solution gas-drive reservoir’s future well performance, the underlying assumption of almost all available correlations is that an oil mobility profile stays the same throughout a reservoir life. However, in practice, this data is often unavailable since one of the functions of oil mobility is saturation distribution. Because of this unavailability, most of the correlations that were developed for future well performance either require more than one flow test data or assume that oil mobility is linear to pressure, which was proven inaccurate by Camacho and Raghavan (1989). Standing (1971) stated that the productivity index of present-day value and productivity index of any future value can be estimated by its mobility function. Similar to all previously available correlations for future well performance prediction, this study also uses Standing’s definition as its fundamental. The main aim of this study is to develop a generalized correlation to describe oil mobility ratio profile from solution gas-drive reservoir which is done by assuming a cubic relationship between normalized pressure and normalized mobility as stated by Ilk et al. (2007). A total of 64 black oil reservoir models were generated and run using CMG- IMEX. From the simulation results, normalized mobility vs normalized pressure data of all reservoir models are plotted into a single graph to get a trendline. The trendline is assumed as general oil profile mobility for solution gas-drive reservoir. The generalized mobility profile and Standing’s equation are then applied to example cases from Kelkar-Cox (1985) and compared to other method which also requires a single flow data test. The new method of estimating future performance estimation yields a lower average percent error.