An efficient prediction and historical matching method for reservoir dynamics using advanced attention-based vision transformer

This paper presents an efficient method for reservoir dynamic prediction and historical matching, utilizing an advanced attention-based vision transformer (AAVT) model to overcome the inefficiencies of traditional numerical simulations with high-dimensional and uncertainties. The deep learning techniques effectively capture the internal relationships between complex geological features and production data, significantly enhancing both computational efficiency and prediction accuracy. The article offers a comprehensive overview of the AAVT model architecture and its self-attention mechanism, emphasizing its strengths in feature extraction and information processing. Through practical case analyses, the model's effectiveness and robustness across various scenarios have been validated, underscoring its potential for dynamic prediction and historical matching. The AAVT model exhibits significant advantages in addressing high-dimensional uncertainties and complex geological structures, achieving improved accuracy while reducing computational costs by approximately 80% compared to traditional methods.