Abstract Pressure testing in very low-mobility reservoirs is challenging with conventional formation-testing methods. The primary difficulty is the over-extended build-up times required to overcome wellbore and formation storage effects. Possible wellbore overbalance or supercharge are additional complicating factors in determining reservoir pressure. This paper addresses the above technical complications and estimates petrophysical properties of low-mobility formations using a newly developed adaptive-testing approach.
The adaptive-testing approach employs an automated pulse-testing method for very low-mobility reservoirs and uses short drawdowns and injections followed by short pressure stabilization periods. Measured pressure transients are used in an optimized feedback loop to automatically adjust subsequent drawdown and injection pulses to reach a stabilized pressure as quickly as possible.
The automated pulse data is used to determine supercharge effects, formation pressure, and mobility via analytical models by analyzing the entire pressure sequence. A genetic algorithm estimates additional reservoir parameters, such as porosity and viscosity, and confirms results obtained with analytical models (reservoir pressure and permeability). The modeled formation pressure exhibits less than 1% difference with respect to true formation pressure, while the accuracy of other parameters depends on the number of unknown properties. As a quicker method to estimate reservoir properties, a direct neural-network regression of pulse-testing data was also investigated.
Synthetic reservoir models for low-mobility formations (M < 1 μD/cp), which included the dynamics of water- and oil- based mud-filtrate invasion that produce wellbore supercharging were developed. These reservoir models simulated the pulse-testing methods, including an automated feedback-optimization algorithm that reduces the testing times in a wide range of downhole conditions. The reservoir models included both simulations of underbalanced and overbalanced drilling conditions and enabled the development of new field-testing strategies based on a priori reservoir knowledge. The synthetic modeling demonstrates the viability of the new pulse-testing method and confirms that difficult properties, such as supercharging, can be estimated more accurately when coupled with the new inversion techniques.