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 - 4
SP - 3417
EP - 3422
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. 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. For missing data, it is recommended to use tree models or  neural networks instead of imputation or constant filling, as incorrect  imputation significantly degrades model performance. The entropy weight  meth­od effectively integrates various correlation analysis results,  providing a better connection with production compared to other approaches.  This research utilizes large-scale field data to extract key parameters  affecting production, supporting the establishment of high precision  prediction models and the optimization of pa­rameters for unconventional  reservoir production forecasts.