## 1 Introduction  
Carbonate rock formations are the most important type of oil and gas reservoir. It is estimated that approximately 60% of the world's oil reserves are located in carbonate reservoirs [1]. According to a report by Schlumberger (2019) [2] of conventional oil reserves in the Middle East are located in carbonate reservoirs. In the case of the Tarim oil field's carbonate rock formations, the recovery rate is less than 20%, and the utilization of the reserves is low, making the deployment of highly efficient new wells of great significance for increasing production. However, well position optimization is a major challenge, as on the one hand, the inhomogeneity of the reservoir will result in serious non-smooth, non-continuous, and non-convex cost functions containing multiple local optimal values [3]. On the other hand, well position optimization problems are often based on reservoir numerical simulation and the scope of the option is determined by the scale and size of the model. To find a more extensive and accurate global optimal solution, large-scale models with small grid sizes are often required, which greatly slows down the convergence speed. The currently commonly used well position optimization algorithms mainly include two categories: gradient-based algorithms and non-gradient-based algorithms. Gradient-based algorithms include mixed integer programming (MIP) [4], multivariate interpolation algorithm (Pan, n.d.), simultaneous perturbation stochastic approximation [5], and the gradient-based finite difference method [PERSON] and [PERSON] (2012), which all rely on gradients to find a better location. However, due to over-reliance on gradients and difficulties in calculating an accurate convergence direction, these methods are easily trapped in local optima. Therefore, due to the nature of the problem [5], gradient-based techniques are not very useful for optimizing well positions. Compared to gradient-based algorithms, derivative-free methods are less prone to getting trapped in local optima [6]. Many nature-inspired derivative-free methods, such as genetic algorithms [7], particle swarm optimization (PSO) [8], differential evolution (DE) [9], bat algorithm (BA) [10], and ant colony optimization (ACO) [11], among others, have been used for well placement optimization problems. These algorithms do not require gradient information, thus do not rely on derivative computation. Moreover, local search algorithms, such as generalized pattern search (GPS) [12] and Hooke-Jeeves directed search (HJDS) [13], have also been utilized. These derivative-free optimization techniques have shown good performance when derivatives are not available or difficult to obtain for optimization problems. In order to achieve better performance, many researchers have combined the advantages of different algorithms to develop many hybrid strategies [14] for solving well placement optimization problems. As the size of the reservoir model increases, the sampling interval range of well deployments also becomes larger, and these algorithms all face the same problem of decreasing computational efficiency and convergence speed. Based on this situation, some research has been conducted to integrate human experience into well deployment work. [PERSON] [15] proposed the black hole concept to constrain the location of new wells and maintain a certain distance between new and old wells. [PERSON] [16] proposed using the effective boundary obtained from geological information to constrain the position of horizontal wells and help them quickly reach a more optimal position. [PERSON] [17] used a convolutional network with physical formula constraints and an evolutionary algorithm for well placement optimization. [PERSON] [18] [PERSON] [19] used geological model attribute maps as a reference to optimize the position of horizontal wells. [PERSON] [20] directly used a two-dimensional graph defined by human experience as a mutation standard to guide the mutation process.