Low-frequency model constrained petrophysical estimation based on canonical correlation analysis

Petrophysical estimation serves as a critical tool for quantitative interpretation in reservoir characterization, bridging the gap between petrophysical and elastic parameters. Traditional approaches, which rely heavily on rock physics models, often face challenges in accurately capturing the complex relationships between these parameters. While statistical methods such as canonical correlation analysis have shown promise in identifying linear relationships and efficiently estimating petrophysical parameters, they tend to overlook low-frequency variation trends, which can lead to unreliable estimation results. To address this limitation, we propose an enhanced method that integrates low-frequency model constraints into the canonical correlation analysis framework. By incorporating a low-frequency model during the standardization process, we effectively eliminate the influence of low-frequency variation trends, strengthening the linear relationship between elastic and petrophysical parameters. Meanwhile, this approach establishes robust statistical relationships between petrophysical and elastic parameters, thereby improving the accuracy of subsurface spatial mapping of petrophysical properties. In both synthetic and field data experiments, the results demonstrate that our proposed method achieves significantly higher accuracy compared to conventional techniques. These findings highlight the reliability and precision of our approach in characterizing petrophysical parameters, offering a valuable advancement for reservoir characterization in complex geological environments.