Hydraulic fracturing is critical process in unconventional reservoir development, where accurate real time monitoring directly impacts operational safety, job efficiency and financial aspect. This study presents a hybrid framework integrating domain specific operational thresholds with supervised machine learning for anomaly detection and multi parameter prediction. In anomaly detection, XGBoost achieved the highest accuracy of 0.99 and F1-score of 0.98, outperforming random forest (0.97), logistic regression (0.65), KNN (0.90) across traditional evaluation metrics. In predictive model, gradient boosting algorithm achieved the highest R2 score of 0.97 and the lowest RMSE, outperforming both linear and ridge regression model used for base model. The inclusion of operational constraints into the machine learning model enhanced reliability, prediction accuracy, robustness ensuring practical and actionable results for hydraulic fracturing processes.
Electric Submersible Pumps (ESP) are among the most widely used artificial lift methods in oil production wells. Despite their effectiveness, ESP systems are prone to unexpected failures that lead to significant production losses, costly workover operations, and extended downtime. Traditional maintenance strategies, su...
W. Mahmud, Masara A. Aldowro· Global Journal of Energy Tec...· 0 citations
Predicting emergencies caused by uncontrolled and sometimes sudden changes in methane concentration within working and adjacent zones of coal mines remains a critical and challenging task, the solution for which can greatly enhance mining safety. This study presents a hybrid machine-learning model trained on real and s...
A. Ivannikov, Igor' Temkin, I. Savelev· Applied Informatics· 0 citations
In shale gas development, Net Present Value (NPV) and Internal Rate of Return (IRR) are influenced by the coupling of multi-source geological and engineering parameters, and quantitative research on the marginal effects and risk thresholds of key parameters remains lacking. A deep feedforward neural network predictio...
Dong Wang, Kai-Xiang He, Huan Cui et al.· Frontiers in Earth Science· 0 citations
Liquefied natural gas (LNG) storage tanks are susceptible to thermal stratification, a phenomenon that triggers rollover events and uncontrolled boil-off gas (BOG) generation, posing significant safety and economic risks. Traditional computational fluid dynamics (CFD) approaches offer mechanistic insight but requir...
Accurate and rapid prediction of groundwater levels (GWL) is essential for effective groundwater management. Machine learning models are efficient tools for GWL prediction, but individual models often suffer from limited generalization due to inherent randomness. This study proposed a stacking-based GWL prediction fr...
Xunzhen Cui, Xiaoxia Du, Haixia Dong et al.· Frontiers in Water· 0 citations
