Supercharged pressures exist when drilling fluid losses (spurt, dynamic and static) invade the near well-bore region and creates a ‘supercharged’ pressure zone that is higher than the reservoir pressure but lower than the wellbore hydrostatic pressure. Due to the overbalanced hydrostatic pressure the fluid invades but cannot be disbursed because of the low mobility of the rock. This creates a near well-bore region with pore pressures between hydrostatic (wellbore) and reservoir pressure. This typically occurs in low mobility formations where the dispersion of the invaded drilling fluids is not efficient. Determining true reservoir pore pressure in these conditions is difficult for formation pressure testing tools (FPT's) which measure elevated pressures above true reservoir pressure in these conditions. Analyzing the change in measured pressures from repeated tests using FPT's may help estimate the true formation pressure.
One characteristic indication of supercharging is successive pressure build-up tests (after small drawdown volumes) that stabilize at lower pressures with each subsequent test as more supercharging fluid is removed from the near well-bore region. The successive decrease in build-up pressure as a function of volume can provide information on the dynamic pressure environment in the near wellbore zone and the reservoir pressures further from the wellbore. Plotting the pressure drop as a function of fluid volume removed from the formation and fitting an exponential decay curve to the data provides an estimate of the reservoir pressure. The curve is optimized using a regression algorithm to find a best match. Because one of the unknown variables is the desired formation pressure, a range of formation pressures are evaluated and a χ-squared error function is minimized, thus approximating the true reservoir pressure.
Numerical simulation models with known formation pressures were set-up with a static supercharged near well-bore environment and various pressure tests were conducted. Analysis was performed on a number of tests to optimize the regression algorithm. The optimized regression provided an indication of the reservoir pressure within 2% of the simulated value. Real data examples were also analyzed with good results.
This analysis technique provides a novel empirical method for estimating reservoir pressures in supercharged environments by investigating the change in build-up pressures in successive tests. The analysis can be accomplished with pressure measurement data from standard FPT's. Furthermore, the individual pressure tests do not need to stabilize because the change in pressure is used nor do the pressure tests need to measure the true reservoir pressure because it is determined by a regression analysis.