Hydrocarbon reservoirs characterization by co-interpretation of pressure and flow rate data of the multi-rate well testing

Pressure transient behavior is among the most important information for characterizing a reservoir, forecasting its future performance, and designing an appropriate recovery scheme. Although a continuous real-time monitoring of reservoir bottom-hole pressure has become a routine task in intelligent wells, complete extraction of the potential information from these valuable sources of data may not be achieved by using traditional interpretation methods. Deconvolution transforms the pressure transient data related to the wells with variable production rates into an equivalent constant rate pressure data with duration equal to the whole duration of the multi-rate test i.e., unit step response. This technique can reveal high valuable information over a distance from the wellbore which may be several orders of magnitude greater than the radius of investigation of individual flow periods. In the present study, a robust and practical deconvolution methodology is developed for extracting the unit step response (USR) from synthetic, noisy and incomplete pressure transient histories pertaining to multi-rate data. Our proposed scheme calculates the USR from those multi-rate well testing data which may contain high levels of noises in both the flow rate and pressure data. A coupled wavelet transform/superposition theorem is the basis of the proposed method. The algorithm has shown an excellent performance for revealing reservoir/boundary models and their associated parameters. (C) 2015 Elsevier B.V. All rights reserved.