A Novel Paradigm for Parameter Optimization of Hydraulic Fracturing Using Machine Learning and Large Language Model

Hydraulic fracturing is a common practice in the oil and gas industry meant to increase the production of oil and natural gas. In this process, appropriate fracturing design parameters are important to maximize the efficiency of fracture propagation. However, conventional fracturing parameter design methods often rely on expert experience or fail to take into account complex geological conditions, resulting in suboptimal parameter design schemes. Therefore, this paper presents PPOHyFrac, a novel paradigm for optimizing hydraulic fracturing parameters with large language model and machine learning, which aims to automatically extract, assess and optimize fracturing parameters. PPOHyFrac uses advanced large language model to perform the extraction of key parameters from hundreds of fracturing design documents, and then refines the extracted data using statistical methods such as missing value imputation and feature normalization. Besides, the techniques in correlation analysis are utilized to identify key influencing factors and finally machine learning methods are implemented to optimize and predict the key influencing factors. This paper also presents a comparative study of five machine learning methods. Experiments show that random forest is the best choice for parameter optimization and can improve the prediction and optimization accuracy of key parameters.

@article{Yang2025,
  title     = {A Novel Paradigm for Parameter Optimization of Hydraulic Fracturing Using Machine Learning and Large Language Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Chunxi Yang and Chuanyou Xu and Yue Ma and Bang Qu and Yiquan Liang and Yajun Xu and Lei Xiao and Zhimin Sheng and Zhenghao Fan and Xin Zhang},
  doi       = {10.14569/IJACSA.2025.01603108},
  url       = {https://doi.org/10.14569/IJACSA.2025.01603108}
}