Summary
Intelligent completions typically include permanent downhole gauges (PDGs) for continuous, real-time pressure and temperature monitoring. If applied adequately, such new technologies should allow anticipation of oil production and an increase of final recovery with respect to traditional completions. In fact, pressure data collected from PDGs represent essential information for understanding the dynamic behavior of the field and for reservoir surveillance. The potential drawback is that the number of data collected by PDGs can grow enormously, making it very difficult, if not impossible, to handle the entire pressure history as it was recorded. As a consequence, it might often be necessary to reduce the pressure measurements to a manageable size, though without losing any potential information contained in the recorded data.
As reported extensively in the literature, long-term data might be subject to different kinds of errors and noise and not be representative of the real system response. Before the data can be used for interpretation purposes, especially if pressure derivatives are to be calculated (for instance, in well-test analysis), an adequate filtering process should be applied.
Multistep procedures based on the wavelet analysis were presented in the literature for processing and interpreting long-term pressure data from PDGs. In this paper, an improved approach largely based on the wavelet algorithms is proposed and discussed for the treatment of pressure data.
All the steps of the procedure, namely outlier removal, denoising, transient identification, and data reduction, were applied to both synthetic and real pressure recordings. Results indicated that the application of the proposed approach allows identification of the actual reservoir response and subsequent interpretation of pressure data for an effective characterization of the reservoir behavior, even from very disturbed signals.
