Traditional source-to-sink analyses cannot effectively characterize deep-time sedimentary processes involving multiple sediment sources and the spatiotemporal evolution of sediment contributions from different sources. In this study, a dynamic, quantitative source-to-sink analysis approach using stratigraphic forward modeling (SFM) is proposed, and it is applied to the Paleogene Enping Formation in the Baiyun Sag, Pearl River Mouth Basin. The built-in spatiotemporal provenance tagging of the model assigns a unique time-source label to sediments from each provenance, making each source's contribution identifiably “labeled” in the simulated formation, and thus enabling a direct precise tracking and high spatiotemporal resolution quantification of such contributions. Five pseudo-wells (from proximal to distal locations) in the Baiyun Sag were analyzed. The simulation results quantitatively represent the varied proportion of contribution of each source at different locations and in different periods and verify the proposed approach's operability and accuracy of the proposed approach. The simulated 3D deposit distribution shows a high agreement with the measured stratigraphic data, validating the model's reliability. Results reveal significant spatiotemporal changes in the Enping sedimentary system. In the late stage of Enping Formation deposition, a distal source supply from the northern part of the sag became dominant, the depocenter migrated northward to the deepwater area, and large-scale deltaic sand bodies extensively progradating into the sag were formed. The modeled 3D deposit distribution indicates that extensive high-quality reservoir sandstones are likely present across the deepwater area of the Baiyun Sag, which are identified as key exploration targets. Compared to traditional static approaches, the SFM-based dynamic simulation markedly enhances the spatiotemporal resolution of source-to-sink analysis and quantitatively captures the sedimentary system's responses to tectonic activity, base-level fluctuations and other external drivers. The proposed approach provides a novel quantitative framework for investigating complex, deep-time, multi-source systems, and offers an effective tool for reservoir prediction and hydrocarbon exploration planning in underexplored deepwater areas.
GAO et al. (Sun,) studied this question.