An Intelligent Optimization Method for Casing Program Design of Extended-Reach Wells

This study aims to integrate extension limit screening, engineering evaluation, and casing-shoe depth search into a decision-support method for offshore extended-reach wells. The method combines open-hole, mechanical, and hydraulic extension limits with fuzzy scoring, analytic hierarchy process (AHP) weights, and a genetic algorithm (GA). The overall well score is used as the scalar search objective, with geological setting windows treated as prerequisites for engineering acceptance. The retrospective dataset comprises 30 completed wells, of which ten extension limit checks and five representative search cases are presented. Nine of the ten completed depths displayed are below the predicted limits; one exceeds its predicted limit by 113 m (1.83%). For Well A1, the open-hole, mechanical, and hydraulic limits are 5680 m, 4500 m, and 6200 m, respectively, making the mechanical limit controlling. The A1 numerical candidate scores 0.8898, below the field program at 0.8956, so the field program remains preferred. For Well A4, the actual four-section program reduces the final drilled interval from 2513.43 m in a hypothetical three-section program to 458.62 m and raises the overall score from 0.8562 to 0.8783. These cases provide retrospective evidence of workflow feasibility, not full predictive validation or proof of global optimality. Application requires conservative engineering screening and subsequent review of geological, trajectory, cementing, tubular-load, and rig constraints.