Research on the scheduling scheme of resource storage locations in deep-sea oil and gas exploitation

Crucial components of the exploration and exploitation of deep-sea oil and gas resources include the identification of pre-disaster emergency resource storage locations and the appropriate scheduling of emergency resources. Frequently, integer linear programming or integer nonlinear programming is used to investigate them. Both techniques may result in optimization, but it is difficult to determine the global optimal solution. Consequently, this study presents a two-stage optimization model of emergency resource storage location-scheduling that not only surpasses the limitations of independent research but also applies an intelligent optimization algorithm to discover the global optimal solution. In the first phase of the model, the objective of optimization is to decrease the emergency response time. Uncertainty and unpredictability are incorporated as marine environmental components in the goal function. Using the analytic hierarchy method, the effect of resource storage and location is evaluated, and the genetic algorithm (GA) and immune algorithm (IA) are used to determine the goal model's optimal solution. The objective of the second stage is to optimize resource scheduling satisfaction, and the resource storage locations determined in the first stage are assigned using a fuzzy trigonometric function formula and mathematical programming algorithm. The two-stage optimization methodology is shown using the deep-sea fire explosion incidence as an example. The results suggest that the transit time of resources from the resource storage site selection points to the operation points calculated by GA is 50% less than that obtained by IA, and the 4 time of IA is at least 5 times that of GA. The overall resource scheduling time is lowered by 41.1% compared to conventional shore-based terminals, the economic cost of an accident is minimized, and the life safety of on-site operators is improved. Throughout 1000 different seasons, the objective ideal value and average value of GA and IA are compared. GA has a slower convergence rate than IA, but its convergence quality is much higher. The quantity of emergency supplies given by each resource storage site may meet the resource need of the operational point. We believe that the model proposed in this study can guarantee the accuracy of prediction results in situations of small sample sizes, and its validity and applicability have been validated.