Interpreting Pressure and Flow Rate Data from Permanent Downhole Gauges Using Data Mining Approaches

Abstract

Abstract The Permanent Downhole Gauge (PDG) is a promising tool for reservoir testing, but has yet to reach its full potential. Generally, conventional well testing methods are most able to utilize small sections of constant flow rate data. However, data mining, a newly developed technique in computer science, is a tool that can reveal the relationship among variables from large volumes of data. The application of data mining algorithms to synthetic and field data has been successful in extracting the reservoir model from variable flow rate and pressure transient data. The application is conducted in two steps. As a first step, the pressure and flow rate data from the PDG are used to train a nonparametric data mining algorithm. The reservoir model is obtained implicitly in the form of polynomials in a highdimensional Hilbert space when the algorithm converges after being trained to the data. In the second step, a constant flow rate input is fed into the data mining algorithm. The data mining algorithm will make a pressure prediction subject to the input flow rate. As the data mining algorithm has already obtained the reservoir model, the pressure prediction is expected to be the reservoir response given the constant flow rate. Therefore, the proposed constant flow rate and the predicted pressure are the reservoir model underlying the variable PDG data. Noisy synthetic data and the real field data were used to test this approach. The method was able to recover the reservoir model successfully. Even at the extreme cases when the flow rate data are very noisy and changing frequently, and in the absence of any shut-ins, the method was still able to extract the reservoir models.