| Citation: | SHI Jiaxin，LI Shaohua，DAI Yunjiao，et al. Geological modeling method for deepwater gravity flow reservoirs under sparse well conditions[J]. Bulletin of Geological Science and Technology，2026，45（5）：325-335 doi: 10.19509/j.cnki.dzkq.tb202604050
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Deepwater oil and gas fields are typically characterized by sparse wells, large well spacing, and limited seismic data resolution. Deepwater gravity flow reservoirs develop multilevel architectural units, with complex spatial distribution and stacking relationships of sand bodies. Conventional modeling methods struggle to simultaneously ensure the rationality of geological patterns and the fine characterization of reservoir heterogeneity, posing challenges for geological reservoir modeling under sparse well conditions.
Taking the gas field A in the Baiyun Sag, Pearl River Mouth Basin as a case study, this study proposed a geological modeling method for deepwater gravity flow reservoirs suitable for sparse well conditions. Under joint well-seismic constraints, this method adopted the core concept of "hierarchical constraint and level-by-level nesting". In the first-level modeling, the interpreted channel complex profiles were transformed into a 3D grid through a spatial mapping mechanism, and a combined deterministic and stochastic approach was employed to construct the channel complex model. In the second-level modeling, for the channel complex model, 3D training images reflecting the morphology and stacking relationships of single channels were constructed. Additionally, a multiple-point geostatistics algorithm was utilized to finely characterize single channels and mudstones. For the lobe complex, an object-based modeling method was adopted to establish the mudstone model. Finally, sedimentary units at all levels were nested and integrated according to hierarchical priority to establish a 3D sedimentary facies model, based on which a reservoir petrophysical model was constructed.
The results demonstrated that, compared with deterministic modeling methods, the proposed method more accurately characterized the heterogeneity and spatial distribution of sand bodies in deepwater gravity flow reservoirs, significantly improving model accuracy.
The research findings provide a reliable theoretical basis and technical support for the exploration, development, and decision-making of deepwater gravity flow reservoirs.
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