In the development of tight gas reservoirs in Chuanzhong BJC Gas Field of the Sichuan Basin, running horizontal casing in ultra-long open-hole section faces challenges. These include large friction prediction errors and high casing buckling risks. These challenges significantly impede both the efficiency and safety of field development. Traditional static segmented friction models fail to accurately predict friction coefficients. The reason is that they cannot track dynamic changes in wellbore inclination, azimuth, and dogleg severity in real time. To address this bottleneck, this study develops a technical system termed AI-based dynamic friction inversion-segmented process optimization. Clustering algorithms are used to divide regions. These regions have low, medium, and high friction characteristics. The simulated annealing algorithm dynamically corrects friction coefficients. Meanwhile, the segmented processes of float collars and drilling fluid density are optimized. Verification was conducted on well J108-2H, which features an open-hole section of 4060.9 m and a horizontal-to-vertical ratio (HD/TVD) of 1.88. Results show that this system significantly reduces the mean absolute percentage error of friction coefficient prediction. It also greatly improves the accuracy of casing running feasibility assessment. As a result, the casing in well J108-2H was run smoothly and efficiently. The research results provide an innovative solution for the safe and efficient development of ultra-long open-hole sections in unconventional gas reservoirs.