Sedimentary microfacies prediction based on multi-point geostatistics under the constraint of INPEFA curve

The D Oilfield in the Songliao Basin has entered a critical phase of hydrocarbon exploration targeting the Quantou Formation. However, challenges persist in achieving precise stratigraphic division, sedimentary cycle characterization, and microfacies prediction. This study aims to enhance stratigraphic resolution and prediction accuracy of sedimentary microfacies to address uncertainties in sand body distribution within dense well pattern areas. Integrated Pattern Recognition and Fuzzy Analysis (INPEFA) was applied to natural gamma logging data from 4,215 wells, combined with maximum entropy spectrum attribute analysis (MESA) for high-precision sequence interface identification. Multi-point geostatistical simulations were implemented using well data and INPEFA-derived sequence stratigraphic frameworks. High-order compatibility algorithms constrained the simulations, with iterative human-computer interaction refining sedimentary microfacies models for three sand groups. Three distinct sedimentary phases were resolved: (1) Sand Group III exhibits rapid water encroachment with narrow, strip-like sand bodies; (2) Sand Group II records gradual lacustrine regression, forming extensive sheet-like sands; (3) Sand Group I reaches maximum flooding conditions, displaying laterally continuous sand bodies with intensified fluvial dynamics. The method predicts sedimentary microfacies under INPEFA curve constraints, which makes sequence interface identification more intuitive and improves cycle division and correlation precision. This multi-point geostatistics-based prediction image accurately reproduces river channel distribution patterns, offering high predictability and presenting a novel approach to characterizing fine sand bodies. This integrated approach not only predicts sedimentary microfacies distribution in dense well pattern areas of the Songliao Basin, but also presents a novel solution to the challenging geological problem of accurately forecasting effective sand body distribution. Furthermore, it provides critical stratigraphic evidence for developing remaining oil potential strategies in the basin, while offering an innovative methodology for reservoir characterization in other mature exploration regions worldwide.