# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
###### Abstract  
In modern oil and gas exploration and development, wellbore trajectory optimization and control is the key technology to improve drilling efficiency, reduce costs, and ensure safety. In the drilling operation of non-vertical wells in complex formations, the traditional static trajectory function, combined with the classical optimization algorithm, has difficulty adapting to the parameter fluctuation caused by formation changes and lacks real-time performance. Therefore, this paper proposes a wellbore trajectory optimization model based on deep reinforcement learning to realize non-vertical well trajectory design and control while drilling. Aiming at the real-time optimization requirements of complex drilling scenarios, the TD3 algorithm is adopted to solve the problem of high-dimensional continuous decision-making through delay strategy update, double Q network, and target strategy smoothing. After reinforcement learning training, the trajectory offset is significantly reduced, and the accuracy is greatly improved. This research shows that the TD3 algorithm is superior to the multi-objective optimization algorithm in optimizing key parameters, such as well deviation, kickoff point (KOP), and trajectory length, especially in well deviation and KOP optimization. This study provides a new idea for wellbore trajectory optimization and design while drilling, promotes the progress and development of intelligent drilling technology, and provides a theoretical basis and technical support for more accurate, efficient, concise, and effective wellbore trajectory optimization and design while drilling in the future. 20 May  
In recent years, scholars at home and abroad have conducted a lot of research and exploration on how to better consider the formation information for wellbore trajectory optimization. There are also studies on optimizing drilling fluid and optimizing drilling tool combinations to improve wellbore stability, reduce drilling risk, and improve drilling speed. At this stage, most studies have combined wellbore trajectory planning with recurrent neural networks in deep learning. [PERSON] [1] (2022) established an intelligent prediction method for wellbore trajectory planning by using the support vector machine algorithm (SVM) and a recurrent neural network (RNN) for wellbore trajectory prediction and trajectory optimization design. Their research shows that the prediction accuracy is obviously better than the traditional wellbore trajectory prediction model. However, the memory of the RNN is flawed. When there is more data to be processed, the initial learned data will be forgotten. In similar research by [PERSON] [2] (2022) and [PERSON] [3] (2023), the superiority of the LSTM model in predicting the well inclination and azimuth in wellbore trajectory planning was verified by comparative experiments. Compared with the RNN, LSTM introduces a variety of structures, such as a memory gate, so it is obviously better in memory. Both [PERSON] [4] (2022) and [PERSON] [5; 6] (2023 and 2024) improved LSTM, but the focus of the two was different. [PERSON] not only introduced asymmetric depthwise separable convolution, a peephole mechanism, and an attention mechanism in LSTM to extract the spatial and temporal characteristics of formation data but also combined LSTM and GAN to construct a simulated drilling environment. [PERSON] et al. 's improvement focused more on model updating. They successively proposed the L2.SSA-LSTM model and the NOA-LSTM model. The sparrow optimization algorithm (SSA) and non-orthogonal acceleration method (NOA) were introduced into LSTM to accelerate model updating. Although [PERSON] et al. 's research made new progress in model update speed, the real-time performance in the actual drilling process is poor, and the model still cannot adapt to the complex drilling environment. [PERSON] et al. [7] developed an end-to-end Di-GRU network to achieve the real-time prediction of a wellbore trajectory through incremental training.