###### Abstract  
With the development of China's oil and gas exploration and development to complex oil and gas fields, the drilling efficiency and safety of complex formations with large hardness and strong abrasiveness have become increasingly significant. Optimizing drilling parameters is an effective means to increase the rate of penetration (ROP) and improve drilling efficiency. However, traditional drilling parameter optimization methods with only a single objective of increasing the ROP lack consideration of the drill string's drag which may also be increased when drilling parameters change. When drilling a horizontal well, increased drag can reduce drilling efficiency. Aiming at this problem, this paper uses the logging data of the oil field as the data source, establishes an intelligent ROP prediction model through the random forest algorithm, and calculates the string drag using the \"hard-string\" model. Finally, the nondominant sorting genetic algorithm-II (NSGA-II), which is a domination-based multiobjective optimization algorithm, is used to optimize the drilling parameters to increase the ROP and reduce the drag at the same time. The optimized drilling parameters guide the drilling operations. We used the proposed method to optimize the parameters during the drilling of a new horizontal well. The results show that the ROP of the horizontal section of the new well increases by 10.3%, and the drag reduces by 4.5% on average compared with the adjacent well.  
drilling parameters optimization; big data; ROP; string drag +
Footnote †: journal: Applied Sciences  
18 October 2022  
Accepted: 15 November 2022  
17 November 2022  
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optimization objectives at the same time for the synergistic optimization of drilling parameters, as shown in Table 1. These works achieved better results than single objective optimization. It can be seen that the method of multiobjective optimization of drilling parameters considering the restriction effect between objectives is more scientific.  
Although drilling parameter optimization methods are developing rapidly, there is still a lack of research on drilling parameter optimization of horizontal wells [14], and the existing drilling parameter optimization methods do not consider the impact of horizontal well drag on drilling efficiency. For horizontal well drilling, the change in drilling parameters will affect the drag, and the drag will also affect ROP. In addition, large drag will also lead to complex downhole accidents such as stick-slip and stuck [15].  
In this paper, according to the characteristics of horizontal well drilling, using oil field big data and machine learning algorithm, the maximum ROP and minimum drag are simultaneously used as optimization objectives of drilling parameter, and the parameter combination of horizontal well drilling is given more scientifically to improve drilling efficiency. Field tests show that the proposed optimization method of drilling parameters can improve the ROP of the horizontal section under the requirement of reducing drag.