Full
QU Linbo,born in 1999,is a Ph.D. candidate. He is mainly engaged in intelligent reservoir prediction and architectural characterization. E-mail: 2023315010@student.cup.edu.cn.
Outline
Shallow-water delta front sand bodies are widely developed and represent important hydrocarbon reservoirs with high exploration potential in continental basins. However,frequent migration and avulsion of distributary channels result in vertically stacked thin interbeds and laterally interwoven distributions,leading to complex architectural patterns. Under sparse well coverage,this complexity makes reservoir prediction highly challenging and less accurate. This study takes the Member 1 of Jurassic Shaximiao Formation in the Tianfu Gas Field,Sichuan Basin,as a case example,and proposes an intelligent prediction method for thin sand bodies by integrating multi-frequency seismic attribute automated machine learning(AutoML)with waveform indication inversion(WII). Laterally,optimized frequency-decomposed seismic data were used to automatically select key attributes and integrate multi-model learning,building nonlinear mappings that improved the correlation between seismic attributes and well-derived sand thickness from 0.64 to 0.82. This enabled quantitative prediction of sand bodies under sparse well control. Vertically,WII introduced facies-controlled constraints and used uranium-free gamma ray as the inversion target,allowing stable identification of sand bodies thicker than 10 m and reliable responses from thin layers of about 4 m,and establishing seismic response templates of architectural element assemblages. Based on these results,four types of architectural elements were identified: distributary channel,residual mouth bar,levee,and interdistributary bay. Furthermore,six stacking patterns including vertical superimposition and lateral accretion were summarized. The results also reveal that,under different depositional cycles,isolated narrow channels and compound wide channels show distinct spatial distributions and vertical stacking characteristics. In three dimensions,a spatial differentiation pattern was clarified,evolving from near-source net-like distributary channels to distal tongue-shaped and lobe-shaped mouth bars. Finally,a depositional architectural model of the shallow-water delta was established. This study achieves fine-scale multi-level architectural characterization under sparse well conditions and provides new insights and methods for efficient exploration and development of complex shallow-water delta reservoirs.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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