###### Abstract  
The development of automation and intelligence in geological core drilling is not yet mature. The selection and improvement of drilling parameters rely mainly on experience, and adjustments are often made after drilling by evaluating the core, which introduces a lag and reduces the drilling efficiency. Therefore, this study first establishes a geological core drilling experiment platform to collect drilling data. Through the constructed geological core drilling experimental platform, the practical data at the drill bit can be directly obtained, solving the problem of the data from the surface equipment in the practical drilling differing from the practical data. Second, the back propagation (BP) algorithm is used to perform the ROP prediction, with weight on bit (WOB), torque (TOR), flow rate (Q), and rotation speed (RPM) as input parameters, and rate of penetration (ROP) as the output. Subsequently, correlation analysis is used to perform the feature parameter optimization, and the effects of bit wear and bit cutting depth on the experiment are considered. Finally, comparison with algorithms such as ridge regression, SVM and KNN shows that the ROP prediction model using the BP neural network has the highest prediction accuracy of 94.1%. The results provide a reference for ROP prediction and the automation of geological core drilling rigs. geological core drilling; back propagation algorithm; drilling speed prediction; BP neural network; ROP +
Footnote †: journal: Applied sciences  
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drilling. [PERSON] et al. [8] proposed using data-driven models to achieve optimization of the drilling mechanical speed and the selection of optimal drilling parameters. Experimental results show that applying a random search algorithm can overcome the feedback delay of data in the computation process of conventional models, thereby increasing the mechanical ROP by an average of 30%. Their team also introduced six mechanistic and machine learning modeling methods to establish drilling speed models and used data from the drilling processes of more than ten different formations to test the accuracy of the models. The research results indicate that machine learning methods have higher drilling speed prediction accuracies. Currently, artificial intelligence algorithms have significant advantages in solving complex multiparameter problems. In geological core drilling, by establishing a complex network of relationships among various drilling parameters, it is possible to achieve accurate prediction and optimization of the mechanical ROP, thereby improving drilling efficiency. However, due to the more complex formation conditions targeted by geological core drilling and the resulting lower economic benefits, there has not been significant application of these methods in this field. The back propagation (BP) neural network is one of the most well-studied neural network algorithms [9], being known for its excellent self-learning, adaptability, robustness, and generalization capabilities. It has received extensive research attention and been applied in many engineering fields. [PERSON] et al. predicted the ROP using AI hybrid models on the directional stage of Kaboud oil-field well #7 [10]. [PERSON] used ant-lion optimization of artificial neural networks for drilling speed prediction [11]. [PERSON] proposed a drilling ROP prediction model that optimizes the weights of the BP neural network by the Improved Chaotic Whale Optimization Algorithm (ICWOA), which improves the accuracy of the drilling speed prediction [12]. The application of machine learning methods for ROP prediction in oil drilling has become quite mature. However, in the field of geological core drilling, ROP prediction still relies on constructing linear models for solutions.