Extracting hydrocarbons from complex, ultra-deepwater and high-pressure/high-temperature wells requires precise control of hydrostatic pressure to avoid well control problems. Among these, a gas kick is one of the most serious events, as it can quickly develop into a blowout with severe consequences for both safety and project cost. Traditionally, the industry has depended on reactive surface-based indicators, such as pit volume and delta flow, for early kick detection (EKD). However, these methods are often limited by data transmission delays and frequent false alarms. This review goes beyond a conventional summary by critically examining the key weaknesses of current EKD technologies. In particular, it highlights major challenges in modern sensor systems, including the difficulty of interpreting ultrasonic signals in multiphase flow and the way formation leakage can hide or distort kick indicators. It also provides a detailed and original link between specific Artificial Intelligence (AI) models and the drilling signals they are designed to analyze. Although recent studies have shown progress in downhole sensing and predictive algorithms, a significant gap still exists between theoretical models and the highly dynamic, multiphase conditions found in real wellbores. This makes it necessary to evaluate EKD technologies considering actual field demands rather than idealized assumptions. To address these limitations, this review proposes several practical directions for future work. These include the development of dynamic, multiphase, acoustic computational fluid dynamics (CFD) models to improve ultrasonic signal interpretation, the standardization of unsupervised AI models supported by synthetic data generation, the integration of unified leakage detection frameworks, the mechanical standardization of Managed Pressure Drilling (MPD) systems, and the adoption of rig-based edge computing to enable faster and more reliable real-time decision-making.
Azab et al. (Fri,) studied this question.
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