Integrating OBN seismic data and machine learning for enhanced fluid discrimination in pre-salt carbonate reservoirs

Accurately identifying reservoir fluids in ultra-deep pre-salt reservoirs remains challenging due to complex lithology, strong heterogeneity, and limited well data. This study develops an integrated workflow combining ocean bottom node (OBN) seismic with pre-stack inversion, AVO analysis, and an LSTM rock physics model to improve fluid prediction. The methodology first extracts P-wave and S-wave impedances through pre-stack inversion, converting these into quantitative fluid indicators for distinguishing hydrocarbons, water zones, and non-reservoir facies. AVO cross-plotting enhances discrimination by exploiting distinct intercept-gradient patterns between oil and brine reservoirs. A key innovation replaces conventional shear-wave modeling with an LSTM network that autonomously learns nonlinear relationships among mineralogy, fluid distribution, and petrophysical parameters without empirical assumptions. Tests using synthetic and field datasets demonstrate superior performance in resolving ambiguous elastic responses characteristic of complex pre-salt reservoirs. The framework significantly reduces fluid identification uncertainties in frontier exploration areas where traditional methods fail, providing critical insights for hydrocarbon exploration while bridging the gap between rock physics theory and practical reservoir evaluation. This data approach offers a robust solution for reservoir characterization in geologically challenging environments, advancing exploration strategies through enhanced diagnostic accuracy.