Self-contained data mining approach for interpreting pressure and flow-rate data from permanent downhole gauges

Abstract

The recording of pressure data by permanent downhole gauges (PDGs) is much like the conventional well tests—in the sense that both of these record pressure response in a well following a flow rate change. The bottleneck of handling PDG data is, unlike conventional well tests, they include highly convoluted pressures reflecting unrestricted fluctuations in a producing well. Moreover, these data are noisy, and large in volume. These difficulties make the PDG data impossible to interpret by familiar conventional analysis techniques. Some recent attempts looked at the problem from a data mining point of view, which revealed the potential of this approach for interpreting PDG data. Among them, a convolution-kernel-based data mining method was relatively more successful. However, expensive computational cost, and some inaccuracy and lack of interpretability in prediction leave room for improving the approach. Data mining is a nonparametric regression technique that involves two stages, namely, training and prediction. During training, the available data are used to teach the algorithm, and upon convergence it is expected to unveil the hidden pattern in data. The trained algorithm then can be used for prediction. However, a mere convergence does not always guarantee a useful correlation between the independent input data and observations. In fact, the success of any kernelized data mining lies in the combination of correct kernel function and appropriate feature selection. Because of the aforementioned method’s relative success in variable flow-rate situations, it was closely inspected to get an insight of the reasons behind its success. The investigation led to three important observations. One, the same complex convolution kernel can be computed using an alternatively formulated method based on the simplest linear kernel. Two, in terms of prediction accuracy, a linear kernel performs better than a polynomial kernel, and therefore should be preferred. Three, empirical nonlinear features can have significant impact on a data mining method’s ability in capturing pressure transient details. Following these observations, a total of 1275 new data mining methods were constructed in present work. A linear kernel was used in these methods, and the feature vectors were constructed by combining some empirically chosen nonlinear terms with the recognized dominant terms pertinent to common reservoir behaviors. The quality of these methods was evaluated on 12 synthetic cases that were carefully designed to mimic the most common real situations. The basis of the quality measurement was simple, how a given method predicts on new data that are totally different from the data it was trained with. Based on the performance analysis, the best method was identified. This method clearly outperforms the aforementioned convolution kernel method, and predictions made by the method on all synthetic cases were very close to the true reservoir behavior— which proves its ability in exploring underlying reservoir models. The method also performed well on real data. Furthermore, the deployment of linear kernel decreases the computational demand significantly.