Machine Learning-Assisted In Situ Monitoring System for Identifying and Predicting Components, Concentrations, and Viscosities of Fracturing Flowback Wastewater

The effective management of fracturing flowback wastewater is critical to oil and gas production sustainability, while its complex and rapidly evolving rheology poses a significant barrier to monitoring and targeted treatment. Traditional offline sampling methods suffer from measurement latency, failing to capture real-time dynamic changes in treatment reactors. To address these limitations, this study develops a novel machine learning-assisted in situ monitoring system integrating ultrasonic time-domain reflectometry (UTDR) to characterize fluid components, concentrations, and viscosity simultaneously. Specifically, the random forest model achieved the highest accuracy (88.0%) in component identification among three tree-based algorithms, while support vector classification (SVC) effectively discriminated concentration levels with an accuracy of 82.4%. For viscosity prediction, the 1D-convolutional neural network (1D-CNN) demonstrated superior performance, achieving an R2 of 0.972. Crucially, interpretability analyses (SHAP and Grad-CAM) confirmed that model decisions align with hydroacoustic principles of attenuation and viscous damping. In dynamic enzymatic degradation tests, the system successfully tracked rapid viscosity transitions with a relative error of less than 13%. This approach provides a high-resolution, cost-effective solution for the intelligent monitoring of fracturing flowback wastewater.