An Artificial Intelligence–Driven Methodology for Predicting Proppant Bed Morphology in Hydraulic Fractures

Summary Predicting the distribution morphology of proppants in hydraulic fractures is a key focus in hydraulic fracturing research. Guided by the principle of similarity, various experimental and numerical models have been developed to analyze proppant bed morphology under different injection parameters. However, despite decades of practical experience and experimental studies, a robust and universally applicable model for proppant transport or a reliable simulation tool for accurately predicting proppant placement has yet to be fully realized. Based on the similarity principles governing narrow fracture geometry and the geometric self-similarity observed in proppant bed evolution, a numerical model was developed to simulate the transport and deposition of proppants within hydraulic fractures. A feature extraction method was used to process time-series simulation data and obtain six geometrical parameters of the bed. The extracted parameters were compiled into a standardized parameter data set. Based on the training data, a deep learning (DL) model was developed to predict bed evolution under varying conditions. The study shows that Open Source Computer Vision Library (OpenCV) techniques can accurately extract geometric feature parameters of numerically simulated sand beds, and the mean error is less than 2%. The bidirectional long short-term memory (BiLSTM) module effectively captures the temporal dependencies in the proppant bed evolution process, making it well-suited for dynamic prediction of bed morphology. The multihead self-attention (MSA) module enhances the model’s ability to identify key timesteps during proppant accumulation, reducing the impact of nonstationary data and improving prediction stability. The Bayesian optimization (BO) module performs global optimization of model hyperparameters, further improving performance and generalization capability. The BiLSTM-MSA-BO model can accurately predict the bed morphology in planar fractures. Based on the model analysis results, the bed morphology evolution follows three patterns under specific injection parameters, including layer upward, mixed deposition, and stack forward. Fluid velocity and proppant particle size influence equilibrium height most, while fluid viscosity has the least impact. The novel method can quantitatively analyze and predict proppant bed morphology within hydraulic fractures, offering an effective tool for completion engineers to optimize injection parameters.