Inversion with stratigraphy-guided deep learning

Abstract

Property estimation in seismic exploration traditionally relies on seismic inversion, which is an ill-posed problem. However, recent advances in deep learning (DL), specifically supervised neural networks, indicate promise for accuracy improvements. Building upon this, stratigraphy-guided deep learning (SGDL) is a novel method that encodes stratigraphic units as discrete features within the DL model. Our primary objective is to evaluate SGDL in a scenario with available geologic data and field data calibration, such as well tops and horizons. We conduct a case study predicting porosity and acoustic impedance from poststack seismic data. Robustness evaluations demonstrate a 20% average enhancement in the correlation for acoustic impedance across 10 test wells from the Volve data set. We find that SGDL inversion outperforms traditional and other DL methods, reaching a 91% correlation for one benchmark blind well. These results offer compelling evidence that the incorporation of stratigraphic units as features in the DL model contributes to further enhancing the accuracy of property estimation. In summary, SGDL represents a novel approach that integrates DL with geologic data, offering significant enhancements in property estimation accuracy within the field of seismic inversion.