Prediction of rock fracture pressure in hydraulic fracturing with interpretable machine learning and mechanical specific energy theory

Hydraulic fracturing stimulation technology is essential in the oil and gas industry. However, current techniques for predicting rock fracture pressure in hydraulic fracturing face significant challenges in precision and reliability. Traditional approaches often result in inadequate accuracy due to the complex and diverse nature of underground formations. However, recent advances in computational power and optimization techniques have enabled the application of machine learning in mining operations, resulting in improved prediction and feedback. In this study, various machine learning techniques are employed to predict hydraulic fracturing pressure based on the concept of mechanical specific energy. Additionally, the study interprets the models through feature importance analysis. The findings suggest that most machine learning models deliver highly accurate predictions. Feature importance analysis indicates that for an approximate assessment of fracture pressure, the characteristics of well depth and torque are sufficient. For more precise predictions, incorporating additional characteristics from the mechanical specific energy framework into the machine learning model is essential. The study emphasizes the feasibility of employing machine learning methods to predict fracture pressure and their usefulness in determining optimal engineering sites. • The selection of features for predicting hydraulic fracturing breakdown pressure based on the Mechanical Specific Energy (MSE) Theory. • To select the optimal machine learning model for predicting hydraulic fracturing breakdown pressure after comparing twenty different models. • To conduct a local interpretability analysis of machine learning results and obtain feature importance.