# Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM
###### Abstract  
The increasing exploration and development of complex oil and gas fields pose challenges to drilling efficiency and safety due to the presence of formations with varying hardness, abrasiveness, and rigidity. Consequently, there is a growing demand for drilling parameter optimization and speed-up technologies. However, existing models based on expert experience can only achieve single-objective optimization with limited accuracy, making real-time adaptation to changing drilling conditions and formation environments challenging. The emergence of artificial intelligence provides a new approach for optimizing drilling parameters. In this study, we introduce the Bi-directional Long Short-Term Memory (Bi-LSTM) deep learning algorithm with the attention mechanism to predict the rate of penetration (ROP). This algorithm improves the ROP prediction accuracy to 98.33%, ensuring reliable subsequent optimization results. Additionally, we propose a coupling optimization algorithm that combines Bi-LSTM with the particle swarm optimization algorithm (PSO) to enhance drilling efficiency through parameter optimization. Our approach aims to maximize drilling footage while maintaining the highest ROP. The optimal solutions obtained are verified through multi-parameter cloud image analysis, yielding consistent results. The application of our approach demonstrates an 81% increase in drilling speed and a 28% reduction in drill bit energy losses. Moreover, the real-time optimization results effectively guide field operations. ROP prediction; drilling parameters optimization; multi-objective optimization; bi-LSTM; PSO +
Footnote †: journal: Applied  
many fields of study such as materials science, the biomedical industry, and finance [1; 2; 3], resulting in fruitful outcomes. Gradually, artificial intelligence has become an independent branch that possesses its own theoretical framework and practical system. Since the birth of artificial intelligence, the theory and technology have increasingly matured, and the application field is constantly expanding. Intelligent algorithms are gradually being applied in the fields of oil drilling and development and have achieved good results [4; 5]. In terms of the ROP prediction model, researchers such as [PERSON], [PERSON], [PERSON] [6; 7; 8; 9], etc., established an ROP prediction model based on KNN, Decision Tree, SVM, and other algorithms by using an integrated algorithm and took the goodness of fit as an evaluation index of ROP prediction. Ensemble machine learning [10; 11] has been widely used in ROP modeling and has achieved results beyond conventional machine learning. [PERSON] and [PERSON] [12; 13] used ANN to establish the ROP of directional wells. Under the condition of sufficient data quantity and high data quality, the ROP prediction accuracy can meet users' needs. [PERSON] [14] and [PERSON] [15] use a variety of genetic algorithms to optimize the neural network algorithm and compare and analyze the effect of the algorithm from the relative error R square, mean square error, and other aspects. [PERSON] [16] used a recurrent neural network algorithm based on logging and path data to predict the ROP with an accuracy of up to 85%. [PERSON] [17] designed a spacecraft telemetry signal anomaly detection model based on LSTM, which determines anomalies through prediction errors, and proposed a non-parametric dynamic anomaly detection threshold determination method, which can achieve a balance between false alarm rate and missing alarm rate. [PERSON] [18] et al. proposed an auto-encoder model based on LSTM, which aims to reconstruct regular time series, and used reconstruction errors for anomaly detection. Regarding parameter optimization, in biomedical material science, the ridge regression method is used to optimize the thermal conductivity of MWCNTs-SiO2/Water-EG nanofluid [19]. The orthogonal distance regression (ODR) algorithm in ANN modeling is used to optimize synthesized hydroxyapatite/ethanolamine for bone tissue [20].