Research on a Dynamic–Static Integration Method for Flooded Layer Identification in Cased Holes

Accurate identification of flooded layers by cased-hole logging is a critical challenge for fine-scale development and enhanced oil recovery in water-flooded oil fields at medium to high water-cut stages. Conventional methods based on single-series logging or two-dimensional crossplot techniques are inadequate for the fine-scale interpretation of complex low-permeability reservoirs. This paper proposes a novel flooded layer identification method through the deep integration of dynamic and static data. The proposed approach organically couples static open-hole logging data (porosity, resistivity, etc.) with dynamic cased-hole logging data (pulsed neutron macroscopic capture cross-section Σ and carbon–oxygen ratio, C/O) within a three-dimensional (3D) crossplot framework. A multidimensional feature parameter space is constructed, and a spatial distance-ratio model is established to quantitatively calculate the flooding index Fw for continuous evaluation of flood level (non-flooded, weakly flooded, moderately flooded, and strongly flooded). Field application in Well X of a low-permeability oil field successfully identified two ambiguous apparent water layers as weakly flooded layers, previously indistinguishable using traditional 2D methods, with interpretation results highly consistent with subsequent production tests. The identification accuracy reached over 90.7%, providing a scalable technical framework for cased-hole flooded layer evaluation in medium-to-low-permeability complex reservoirs.