Intelligent Inversion Analysis of Drilling Gas Kick Characteristic Parameters and Advanced Predictions of Development Trends

Summary Gas kicks are a common and complex occurrence in oil and gas exploration and development. Obtaining the characteristic parameters promptly and accurately after a gas kick occurs is of utmost importance. The kick characteristic parameters are essential foundational data for accurately understanding the multiphase flow state within the wellbore, analyzing kick development trends, and formulating well-killing design. Traditional methods for determining gas kick characteristic parameters often involve trial estimation, a process that can introduce significant arbitrariness. Alternatively, using specialized equipment for measurement and analysis is an option, but it can be time-consuming and limited by technology and cost constraints. This paper proposes an intelligent inversion method for kick characteristic parameters based on surface logging data, including standpipe pressure and mud pit volume. First, a gas kick simulation model is developed based on wellbore multiphase flow theory. This model can accurately simulate the changes in surface logging parameters that occur when a gas kick occurs. Next, the particle swarm optimization algorithm is used to optimize the kick parameters, such as the gas-influx index and pore pressure, in conjunction with the gas kick simulation model. The optimization evaluation criterion is the Fréchet distance, which is used to identify the calculated curve that is most similar to the actual logging parameter change curve. Subsequently, kick trends can be predicted using the simulation model based on these kick parameters. The case analysis demonstrates that the method proposed in this paper can rapidly acquire the gas kick characteristic parameters based on the early change characteristics of logging parameters. It effectively simulates and reproduces the true distribution of wellbore fluids, enabling advanced prediction of kick development. This approach helps in preparing preventive measures in advance, reducing the risk of accidents, and minimizing financial losses.