An improved treatment of long-term pressure data for capturing information

Intelligent completions typically include permanent downhole gauges (PDG’s) for continuous, real-time pressure and temperature monitoring. If adequately applied, such new technologies should allow anticipation of oil production and increase of final recovery with respect to traditional completions. In fact, pressure data collected from PDG’s represent an 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 PDG’s 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 extensively reported 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. Multi-step procedures based on the wavelet analysis were presented in the literature for processing and interpreting long-term pressure data from PDG’s. 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 outliers removal, denoising, transient identification, and data reduction were applied both to synthetic and to 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.