Sami Eyuboglu

Petrophysical Properties of Unconventional Low-Mobility Reservoirs (Shale Gas and Heavy Oil) by Using Newly Developed Adaptive Testing Approach

Pressure testing in very-low-mobility reservoirs is challenging with conventional formation-testing methods. The main difficulty is the over-extended buildup 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 in order 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 faster 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 D/cp) which included the dynamics of waterand oilbase mud-filtrate invasion that produce wellbore supercharging were developed. These reservoir models simulated the pulsetesting 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. Introduction Formation pressure is a fundamental key to assess the hydrocarbon yield of a reservoir. Without an estimate of the formation pressure, there is a great deal of uncertainty in a field’s development and the investment required. Virtually all the methods used to calculate the net amount of recoverable hydrocarbon are highly dependent on the initial formation pressure (Snyder 1971; Sullivan et al. 1988; Mason 1987; Bennett et al. 1975). Field development optimization also depends on formation pressure estimates to verify reservoir depletion and delineate the producing intervals’ connectivity. There have been attempts to find the fundamental properties of tight sand, shale gas, and heavy oil reservoirs (Dastidar et al. 2007; Abu Omokaro et al. 2011; Shabro et al. 2011; Kundert et al. 2009; Galford et al. 2000). However, rarely reported in literature is a study on the pressure transient analysis methods applied to packer and probe-type formation testing for these types of reservoirs yielding the true formation pressure. When a typical drawdown and buildup test is applied, the pressure transient takes too much buildup time to resolve using conventional analysis or a history match to be of practical value in these very-low-mobility reservoirs. With the introduction of the unconventional automated pulse-test method for lowmobility formations (Hadibeik et al. 2012), it is possible to obtain a pressure response that can be used to determine the initial reservoir pressure and permeability in a practical time frame, usually less than 1 hour. The pressure transient analysis can