Lost circulation remains a major challenge during well construction, often leading to non-productive time, increased material consumption, and additional treatment costs. In field practice, the selection of lost circulation materials (LCMs) is still largely based on empirical rules or laboratory testing; however, these approaches are not always suitable for rapid decision-making under variable downhole conditions. This study presents a physics-guided surrogate modeling framework for predicting fracture sealing performance and supporting injection strategy selection. The approach combines laboratory observations with coupled computational fluid dynamics and discrete element method (CFD-DEM) simulations to represent both measured behavior and a broader range of mechanically consistent sealing scenarios. The final dataset included 300 cases, comprising 45 physical experiments and 255 CFD-DEM-generated synthetic cases. A hybrid machine learning architecture based on Temporal Convolutional Networks and Artificial Neural Networks was developed to predict sealing pressure under different material and fluid conditions. The model achieved an R2 of 0.89 and a mean absolute percentage error of 6.4%, while showing 94% agreement with laboratory-based recommendations for injection strategy. The proposed framework can therefore serve as a rapid engineering support tool for preliminary formulation screening and a more computationally efficient digital workflow for fracture sealing design in drilling operations.
Shmoncheva et al. (Sun,) studied this question.