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.
Gong et al. (Wed,) studied this question.
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