TY - JOUR
TI - Analysis of controlling factors for hydraulic fracturing parameters and accumulated production using machine learning
AU - Zhu Zhihua
AU - Hsu Maoya
AU - Li Chang
AU - Dai Jiacheng
AU - Xie Bobo
AU - Ma Zhengchao
AU - Wang Tianyu
AU - Li Jie
AU - Tian Shouceng
JN - Thermal Science
PY - 2024
VL - 28
IS - 2
SP - 1155
EP - 1160
PT - Article
AB - This study, based on static data from over a thousand fracturing wells, employs data governance, data mining, and machine learning regression uncover principal controlling factors for production in the fracturing context. Preprocessing methods, including outlier identification, missing value imputation, and label encoding, address the field data challenges. Correlations among geological, engineering, and production parameters are analyzed using Pearson coefficient, grey correlation, and maximum mutual information. The AutoGluon framework and SHAP post-explanation method compute feature importance. Utilizing multiple evaluation methods, the entropy weight method comprehensively scores and ranks the principal controlling factors. A machine learning production prediction model is established for validation. Results show that DBSCAN achieves better accuracy in identifying field anomaly data.