Integrating physics and machine learning for unified seismic forward modeling and reservoir property inversion

In petroleum exploration and production, accurate reservoir characterization and seismic modeling depend on linking macroscale seismic data with microscale reservoir properties. Prior research has predominantly concentrated on forward modeling or inversion processes in isolation. As a result, a comprehensive multiscale framework that seamlessly integrates both approaches remains absent. In this study, to address this gap, a machine learning (ML)-based petrophysical inversion method was developed. This method was integrated into a unified multiscale workflow by combining rock physics modeling, seismic modeling, and seismic inversion techniques. This study was based on synthetic data. Initially, predefined reservoir petrophysical parameters were used as inputs to rock physics equations to forward model elastic parameters. Following this, seismic forward modeling was performed to generate seismic amplitude-versus-offset (AVO) data. Subsequently, seismic AVO inversion was carried out to recover elastic parameters from the AVO data, which were then converted into reservoir petrophysical parameters using ML techniques. As a result, forward modeling revealed that porosity (𝜙) significantly affects seismic AVO responses, whereas clay volume (C) and water saturation (Sw) had minimal impact. Conversely, seismic AVO inversion, constrained by wavelet effects and input uncertainties, produced a filtered and biased representation of elastic parameters. This compromised the accuracy of subsequent ML-based petrophysical inversion, particularly for Sw in oil reservoirs and C in gas reservoirs. Consequently, 𝜙 inversions demonstrated high reliability, whereas Sw and C predictions showed greater uncertainty. Therefore, by integrating petrophysics with ML, the proposed methodology effectively bridges micro-properties with macro-seismic signals, offering a precise and unified multiscale approach for quantitative seismic modeling and reservoir characterization. Furthermore, the present study capitalizes on rock-physics-generated data, providing ground truth and enabling rigorous, multiscale uncertainty analysis beyond the capabilities of isolated methods. Additionally, it can be seamlessly applied to real-field data. Finally, a comparative discussion between ML-based and physics-based methodologies was performed, leading to the recommendation of implementing explainable artificial intelligence (XAI) for improved interpretability and prediction performance.