Risk Assessment Framework for Gas Kick Events in Complex Drilling Environments: Methods, Metrics, and Mitigation Measures

Abstract

Gas kick events remain a recurring operational challenge in drilling, especially in complex trajectories where pressure windows are narrow and operational variability is significant. Modern wells with extended reach, high-pressure high-temperature conditions, and managed-pressure drilling introduce coupled physical and decision-making complexities that motivate an integrated risk view. This paper develops a structured framework for risk assessment of gas kick events that combines mechanistic modeling of wellbore hydraulics, probabilistic metrics, and data-driven detection. The framework covers gas influx initiation, transient multiphase flow in the wellbore, and surface detection signals under realistic monitoring constraints. Deterministic models provide pressure and flow predictions that are interpreted within a probabilistic setting to quantify likelihoods of operational exceedances such as loss of primary well control or approach to fracture limits. Statistical and machine-learning components are used to integrate heterogeneous sensor streams and to derive early warning indicators calibrated to site-specific noise and operational patterns. The proposed structure supports quantitative risk indices that can be updated in real time as measurements are acquired and operational conditions evolve. Emphasis is placed on explicit representation of uncertainty, including variability in formation pressures, friction factors, equipment response, and human decision processes. The paper discusses numerical implementation aspects, including stability constraints for transient simulators and their interaction with real-time inference and decision support. The overall framework is intended to be adaptable across different drilling environments and operational philosophies, and to provide a consistent basis for evaluating mitigation measures such as kick tolerance design, managed-pressure strategies, and automated shut-in logic.