Permanent downhole gauges (PDGs) can provide a continuous record of flow rate and pressure, which provides extensive information about the reservoir and makes PDG data a valuable source for reservoir analysis. In previous work, it has been shown that kernel-ridge regression-based machine learning is a promising tool to interpret pressure transients from a single PDG. In this work, the machine-learning framework was extended to two applications: multiwell testing and flow-rate reconstruction.
Introduction
Analysis of PDG data is challenging because of the inherent characteristics of the data, including continuously variable flow rate, noise, and the large data volume. Until now, most efforts in PDG-data analysis have been concentrated on pressure-transient analysis on single wells, although there have also been some studies on temperature-transient analysis and multiwall-data analysis. Recently, however, there have been some attempts to apply machine-learning techniques for PDG-data analysis.
