Details
Originalsprache | Englisch |
---|---|
Aufsatznummer | 100173 |
Fachzeitschrift | Rock Mechanics Bulletin |
Jahrgang | 4 |
Ausgabenummer | 2 |
Frühes Online-Datum | 30 Dez. 2024 |
Publikationsstatus | Veröffentlicht - Apr. 2025 |
Abstract
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.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
- Erdkunde und Planetologie (insg.)
- Geologie
- Erdkunde und Planetologie (insg.)
- Geotechnik und Ingenieurgeologie
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
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in: Rock Mechanics Bulletin, Jahrgang 4, Nr. 2, 100173, 04.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Prediction of rock fracture pressure in hydraulic fracturing with interpretable machine learning and mechanical specific energy theory
AU - Zhuang, Xiaoying
AU - Liu, Yuhang
AU - Hu, Yuwen
AU - Guo, Hongwei
AU - Nguyen, Binh Huy
N1 - Publisher Copyright: © 2024 Chinese Society for Rock Mechanics & Engineering.
PY - 2025/4
Y1 - 2025/4
N2 - 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.
AB - 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.
KW - Fracture pressure
KW - Hydraulic fracturing
KW - Interpretable machine learning
KW - Mechanical specific energy
KW - Nonlinear regression
UR - http://www.scopus.com/inward/record.url?scp=105001014745&partnerID=8YFLogxK
U2 - 10.1016/j.rockmb.2024.100173
DO - 10.1016/j.rockmb.2024.100173
M3 - Article
AN - SCOPUS:105001014745
VL - 4
JO - Rock Mechanics Bulletin
JF - Rock Mechanics Bulletin
IS - 2
M1 - 100173
ER -