石油与天然气地质2025，Vol.46Issue(3)：860-875,16.DOI:10.11743/ogg20250311
人工智能驱动的陆架砂体地震沉积学表征
AI-driven seismic sedimentological characterization of shelf sand bodies:A case study on the Neogene Zhujiang Formation,Huizhou Sag,Pearl River Mouth Basin
摘要
Abstract
Neritic shelf sand bodies generally hold favorable geologic conditions for the formation of lithologic traps.However their small reservoir thickness(<1/8 wavelength),rapid lateral variation,and complex lithologies,lead to the sand body prediction results highly ambiguous.In this study,we investigate shelf sand bodies ZJ3A and ZJ3B,the major pay zones within the 3rd member of the Neogene Zhujiang Formation(also referred to as the Zhu 3 Member;confined by third-order sequence boundaries SB1 and SB2)in the Huizhou Sag,Pearl River Mouth Basin(PRMB).Using well logs,as well as data from core and thin section observations,we establish quantitative lithological interpretation standards and select the optimal seismic attributes sensitive to sand body morphology.Accordingly,several artificial intelligence(AI)algorithms are employed for fitting and training based on multiple seismic attributes.As a result,an AI-driven seismic sedimentological characterization system is developed.The results indicate that the fitting of multiple seismic attributes using the random forest(RF)algorithm yielded the most accurate prediction for ZJ3A.The characterization results reveal that this sand body comprises predominantly asymmetric geomorphological units characterized by wide southern parts and narrow northern parts.These units exhibit a maximal length of up to 17.15 km and an average area of 11.23 km2.In contrast,the boosted regression tree(BRT)model yielded the optimal prediction for ZJ3B.The characterization results show that this sand body consists primarily of symmetric geomorphological units characterized by roughly the same widths in the northern and southern parts.These units are small in scale,with an average area of 6.21 km2.The seismic geomorphologies of ZJ3A and ZJ3B indicate the dynamic modification effects of unidirectional coastal currents and bidirectional tides,respectively.Therefore,their seismic geomorphologies jointly reflect the differential hydrodynamic response mechanisms of coastal currents and tidal dynamics during sea-level fluctuations.Seismic sedimentological research combined with fitting and selecting the optimal seismic attributes using multiple AI algorithms can enhance the seismic prediction accuracy of sand bodies.Furthermore,the quantitative characterization of seismic geomorphological units holds implications for the depositional hydrodynamic research of the study area.The results of this study will provide a robust geological basis for selecting the optimal lithologic trap targets subsequently.关键词
人工智能算法/地震沉积学/陆架砂体/珠江组/惠州凹陷/珠江口盆地Key words
artificial intelligence(AI)algorithm/seismic sedimentology/shelf sand body/Zhujiang Formation/Huizhou Sag/Pearl River Mouth Basin分类
能源科技引用本文复制引用
葛家旺,陈聪,刘培,赵晓明,易震,甄艳,张安,唐小龙..人工智能驱动的陆架砂体地震沉积学表征[J].石油与天然气地质,2025,46(3):860-875,16.基金项目
四川省自然科学基金项目(2023NSFSC0810) （2023NSFSC0810）
企业协作项目(CCL2023SZPS0071). （CCL2023SZPS0071）
