A new method for identifying water-flooded layers in low-resistivity reservoirs

Abstract Precise identification of water-flooded intervals is critical for optimizing reservoir development, particularly during the mid-to-late stages of water injection in oilfields. This study uses the Guantao Formation in Block X as a case study, where the prevalence of clay-rich lithologies and complex pore structures has led to the widespread occurrence of low-resistivity oil reservoirs. These conditions pose significant challenges to conventional logging interpretation techniques. To address this, the study investigated the underlying mechanisms responsible for low resistivity through integrated rock physics experiments. Building on these insights, an enhanced logging-based evaluation workflow was proposed to more accurately identify water-flooded zones. First, a lithology-based correction was used to the Simandoux equation to improve the estimation of mixed water resistivity under low-resistivity conditions. Secondly, the extreme gradient boosting machine learning algorithm was employed to predict effective porosity, thereby reducing dependence on nuclear magnetic resonance logging and improving evaluation efficiency. Finally, a cross-plot technique combining ΔRD and Swz-Swir was introduced to quantitatively classify water-flooded intervals. Validation using production well data from Block X yielded an interpretation accuracy of 90.6%, demonstrating significant improvement in identifying water-flooded zones in low-resistivity formations. Furthermore, successful application in adjacent blocks confirms the method’s transferability. Overall, this research offers a robust and scalable workflow for log-based reservoir evaluation in complex resistivity environments, providing critical insights for remaining oil prediction and improved reservoir management in similar geological settings.