Automated well operations is a rapidly growing area with recent progress in automated drilling extending now into automated well monitoring and control during production operations. In reservoir engineering, although the industry continues to guide decision making processes mainly based on physics-based models and simulations, the focus of further developments of the industrial workflows has shifted towards hybrid solutions incorporating machine learning and big data analytics. Development of such solutions requires new approaches to integrate the reservoir physics into the workflows suitable for machine learning and big data analytics. In this paper, we apply and test new metrics for permanent well monitoring developed based on time-lapse pressure transient analysis, called PTA-metrics. These metrics, inheriting reservoir mechanics gained from PTA, remain comparatively simple and suitable for automated workflows. The metrics have been tested on real well data from sandstone and carbonate fields, including slanted injection and horizontal production and injection wells. The testing has confirmed its capabilities in well monitoring separating reservoir from well-reservoir connection contributions to well performance. Application of the metrics enables on-the-fly well monitoring and alarming on well performance issues highlighting the issue origin: either a reservoir or a well-reservoir connection. At the same time, the testing also revealed that reliable application of the metrics depends on the patterns developed by time-lapse pressure transient responses and their Bourdet derivatives. It was shown that the PTA-metrics give reliable results for stable patterns, while change in the pattern may reduce their reliability. The paper concludes with a discussion of ways for application of the metrics in every-day well and reservoir monitoring practice as well as their integration in automated data interpretation workflows developed in the industry.