Innovative AIML Approaches for Autonomous Pressure-Point Detection During Formation Evaluation & Pressure-Transient Analysis

Abstract

Pressure transient analysis is one of the most important tools used by reservoir engineers to understand how fluids move inside the subsurface. Even though the theory behind pressure analysis has existed for many decades, the way it is applied in practice still depends heavily on human judgement, manual plotting, and experience-based interpretation. With the rapid growth of digital pressure sensors and long-term monitoring systems, engineers are now faced with extremely large and complex pressure datasets that are difficult to analyse using traditional methods. In this work, a complete artificial intelligence–based framework is presented to automatically analyse pressure data in a way that still follows physical reservoir principles. The framework identifies pressure stability, builds pressure derivatives, classifies flow regimes, and generates clean datasets that can later be used for advanced modelling. All steps are implemented using open-source tools, and only publicly available pressure datasets are used. The results show that the system can reliably identify meaningful pressure behaviour while avoiding common interpretation mistakes. This work demonstrates that artificial intelligence, when guided by physics, can make pressure transient analysis easier, faster, and more consistent without removing engineering understanding.