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.
A machine learning–based approach for predicting potential ESP failures using historical SCADA operational data and demonstrates that tree-based ensemble models achieve exceptional classification accuracy up to 99.96%, significantly outperforming the baseline Logistic Regression model.
W. Mahmud, Masara A. Aldowro· Global Journal of Energy Tec...· 0 citations
The authors propose an ensemble method comprising staged data preprocessing, generation of physically meaningful features, and weighted ensembles for both regression and classification, which indicates the feasibility of pilot application for dynamic early warning, which can substantially reduce coal mine accident risk...
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
This study proposed a proactive predictive engine coupling transient seepage modelling with machine learning algorithms to address the limitations of reactive slope stability monitoring, and operationalized a dynamic tiered stability threshold, mapping the critical uncertainty zone between permanent stability and impen...
Muhammad Arif Azhar Zaihan, M. K. Abu Talib, Zaihasra Abu Talib et al.· Transportation Infrastructur...· 0 citations
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