An Improved Treatment of Long-Term Pressure Data for Capturing Information

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.

Introduction

Usually, the pressure data is acquired when production tests are performed, during which the well should be produced at a constant rate to allow for analytical interpretation. Because typical test durations range from a few hours to a few days, pressure data are collected over short periods of time and thus only allow the description of limited portions of the reservoir.

Permanent pressure monitoring represents a different and much more effective approach for reservoir characterization and surveillance because both the reservoir and well behavior are overseen continuously in real time by means of PDGs (Baker et al. 1995). On the other hand, for a number of reasons (such as workover, stimulation, and malfunction of the acquisition system), pressure data collected by PDGs can contain extraneous pieces of information that are not representative of the real dynamic behavior of the reservoir. Therefore, the possibility to effectively use the collected pressure data hinges on the application of an efficient treatment and interpretation process, so as to capitalize on the information available for best exploiting the reservoir potential.

The procedures proposed in the literature for processing and interpreting long-term pressure data from PDGs are based on the wavelet analysis (Athichanagorn et al. 1999; Khong 2001; Ouyang and Kikani 2002). The work presented in this paper outlines an improved procedure for pressure-data treatment and analysis. In chronological order, this procedure consists of outlier removal, denoising, transient identification, and data reduction. The main steps of the procedure, outlier removal and denoising, are still based on the wavelet analysis, but the applied algorithms were selected on the basis of a rigorous mathematical review (Mallat 1998; Goswami and Chan 1999). New criteria were developed for the transient-identification process because the method proposed in the literature, which was based on wavelet analysis, did not seem to provide satisfactory results (Athichanagorn et al. 1999; Khong 2001; Ouyang and Kikani 2002).