Abstract:Lost circulation accidents are common during the drilling process due to the influence of geological environment, drilling technology, and other factors. This paper proposes an improved sparrow search algorithm (ISSA) optimized support vector machine lost circulation prediction method to prevent lost circulation accidents and reduce the losses caused by drilling accidents. Firstly, an improved adaptive nonlinear inertia decreasing weight is introduced to improve the global search ability of the Algorithm; Secondly, Levy flight strategy is introduced into the vigilance position update formula to reduce the risk of falling into local optimization.The Sparrow Algorithm (SSA), Genetic Algorithm (GA), Grey Wolf Algorithm (GWO), and improved Sparrow Search Algorithm (ISSA) were compared on 8 benchmark functions to verify the improved algorithm's optimization ability. The results show that the improved sparrow search algorithm (ISSA) is superior to other algorithms in terms of optimization accuracy and stability. Finally, the improved sparrow search algorithm is used to optimize the penalty and kernel parameters of the support vector machine (ISSA-SVM) in order to predict lost circulation accidents. The results show that the prediction accuracy of ISSA-SVM is 97.7654, which is higher than that of Sparrow Search Algorithm (SSA)-SVM, Genetic Algorithm (GA)-SVM and Grey Wolf Algorithm (GWO)-SVM, it can effectively and rapidly predict lost circulation accidents and improve drilling efficiency and reliability.