Enhancing fracture geometry monitoring in hydraulic fracturing using radial basis functions and distributed acoustic sensing

Accurate identification of fracture geometry in hydraulic fracturing is essential for understanding fracture propagation, optimizing stimulation design, and predicting production performance. Distributed acoustic sensing, as a high-resolution near-wellbore monitoring technique, provides rich spatiotemporal data for real-time observation of fracture responses. However, reconstructing fracture geometry from distributed acoustic sensing measurements remains challenging due to high model dimensionality, ill-posed inversion processes and substantial computational costs. This study presents a fracture geometry inversion framework based on radial basis function, in which the fracture width distribution is represented using a small number of radial basis function modes. Owing to the intrinsic smoothness and symmetry of radial basis function, the method eliminates the need for explicit regularization terms, thereby simplifying the objective function and improving inversion stability. This approach significantly reduces the number of inversion parameters while enhancing both accuracy and physical consistency. Applications to a synthetic benchmark model and real field data from the hydraulic fracturing test site demonstrate that the radial basis function-based method consistently outperforms conventional fullparameter inversion approaches, in terms of fitting accuracy and computational efficiency. The proposed method provides a structurally informed and computationally efficient modeling framework for high-dimensional fracture inversion, offering a promising solution for real-time fracture monitoring and parameter estimation in hydraulic fracturing operations. Document Type: Original article Cited as: You, S., Liao, Q., Yue, Y., Tian, S., Li, G., Patil, S. Enhancing fracture geometry monitoring in hydraulic fracturing using radial basis functions and distributed acoustic sensing. Advances in Geo-Energy Research, 2025, 16(3): 260-275. https://doi.org/10.46690/ager.2025.06.06