Dynamic Prediction of Fracture Propagation in Horizontal Well Hydraulic Fracturing: A Data-Driven Approach for Geo-Energy Exploitation

Abstract

Accurate and efficient prediction of hydraulic fracturing fracture propagation is of utmost importance for unconventional reservoir development. Traditional numerical simulation methods require specialized domain knowledge and technical expertise, and machine learning approach can be an alternative predictive tool for fracture propagation. Additionally, existing neural networks cannot be directly applied to hydraulic fracturing scenarios influenced by multiple coupled factors. Thus, this study presents an innovative approach, "AE-ATT-ConvLSTM", that integrates an additional Convolutional Layer, Autoencoder, and an Attention Mechanism Feature Fusion Layer into the architecture of a Convolutional Long Short-Term Memory (ConvLSTM) sequential image prediction network. This approach aims to predict fracture propagations in different fracturing stages of horizontal wells. The proposed model is trained on a sample dataset consisting of 5000 instances of hydraulic fracturing in horizontal wells. The dataset includes crucial data such as fracture propagation images, wellbore perforation images, natural fracture images, pumping schedules, and reservoir properties. The model achieves remarkable results, with an MSE less than 15×10-5, a maximum SSIM of 0.93, and an average FMAE less than 48. The research findings demonstrate that this method significantly enhances prediction efficiency and provides new insights for predicting fracture morphology and optimizing fracturing design.