Formation mechanisms and play fairway prediction of deep tight-sand reservoirs: A case study of the Yingcheng Formation in the Fulongquan fault depression, Songliao Basin

As hydrocarbon exploration in the Songliao Basin progressively shifts from conventional to unconventional targets and from shallow to deep horizons, substantial breakthroughs and significant advancements have been achieved in the emerging area of tight gas exploration in the basin. However, tight gas exploration and development in the Yingcheng Formation of the Fulongquan fault depression remain restricted due to the limited previous studies on tight-sand reservoirs in this formation. In this study, integrated analytical techniques, including high-pressure mercury injection (HPMI), thin-section petrography, scanning electron microscopy (SEM), and X-ray diffraction (XRD), are employed to systematically investigate the petrophysical characteristics of the reservoirs and the primary factors controlling high-quality reservoirs. The results indicate that the reservoir spaces within the Yingcheng Formation in the Fulongquan fault depression are dominated by secondary pores formed by acid dissolution. The physical properties of the reservoirs are jointly governed by mechanical compaction and post-depositional diagenetic processes. Among these, acid dissolution is identified as the most favorable diagenetic process. Effective secondary pores can be generated through acid dissolution only when three conditions are met: an abundant supply of organic acids, favorable dissolution sites characterized by relatively high feldspar content, and an open fluid-flow system with substantial reservoir thickness and well-developed microfractures. Reservoir classification is conducted using HPMI data and fractal dimension analysis, with three classes of reservoirs identified. Accordingly, a comprehensive reservoir evaluation parameter (A) is established to delineate different classes of reservoirs. Based on the comprehensive classification criteria, play fairways are predicted by integrating single-well analysis with sedimentary facies distribution using a superposition method.