Implementation of Hybrid Genetic-Ant Colony Algorithm (GACA) for Solving Highly Nonlinear Cuttings Transport Models in Directional Foam Drilling

Kum-Hyok Choe, Yong-Nam Kim

Directional foam drilling is widely applied in the development of complex oil and gas resources due to its advantages of low formation damage and high drilling efficiency, but the highly nonlinear cuttings transport model in this process poses great challenges to parameter optimization. To solve this problem, a hybrid genetic-ant colony algorithm (GACA) is proposed, which integrates the global search capability of genetic algorithm (GA) and the local search advantage of ant colony optimization (ACO). First, the mathematical model of cuttings transport in directional foam drilling is established, considering the coupling effects of foam rheological properties, wellbore geometry, and cuttings movement characteristics. Then, the GACA is designed by optimizing the encoding mechanism, adaptive genetic operator, and pheromone update strategy to adapt to the high-dimensional and multi-constraint characteristics of the model. Comparative experiments are carried out with GA, ACO, particle swarm optimization (PSO), simulated annealing (SA), and marine predators algorithm (MPA) in terms of convergence speed, solution accuracy, and stability. The results show that GACA has a 15.3%-32.7% higher convergence speed than single intelligent algorithms, and the solution accuracy is improved by 8.9%-21.4%, which can effectively avoid falling into local optimum. Field application in a directional well in the Anju Basin, DPRK shows that the optimized drilling parameters by GACA reduce the cuttings bed height by 31.2% and the annular pressure loss by 18.7% compared with field experience parameters. This study provides a reliable optimization method for solving highly nonlinear cuttings transport models in directional foam drilling and lays a theoretical foundation for improving wellbore cleaning efficiency and drilling safety.