Prediction of Permian karst reservoirs in the Yuanba gas field, northern Sichuan Basin, China

地质学 喀斯特 地貌学 岩性 沉积沉积环境 背斜 天然气田 天坑 碳酸盐岩 构造盆地 沉积岩 地球化学 古生物学 化学 有机化学 天然气
作者
Yuxin Hao,Rebecca Bell,Ying Rao,Rui Fan,Yanghua Wang
出处
期刊:Marine and Petroleum Geology [Elsevier BV]
卷期号:154: 106319-106319 被引量:1
标识
DOI:10.1016/j.marpetgeo.2023.106319
摘要

Karst reservoir has great hydrocarbon potential, however karst reservoir prediction is inhibited by the strong lateral and vertical heterogeneity of karstification which in turn results in recognition difficulty when geological and geophysical methods are used in isolation. Combined geological and geophysical methods, including core observation, thin section analysis, well log interpretation, seismic attributes and seismic inversion are applied to understand the depositional environment, types of karstification, the geophysical response and distribution of karst reservoir and the controls over and evolution model of karst reservoir in Permian Yuanba gas field, northern Sichuan Basin. The results show that there are four types of major seismic facies recognized in the study area, which correspond to platform margin reef, carbonate platform, platform margin inter-bay and platform margin slope. Karstification can be divided into 3 zones: the supergene karst zone, the vertical seepage zone and the horizontal underflow zone, among which the supergene karst zone have the strongest karstification. Karstification is highlighted with low seismic impedance, low Poisson's ratio, and high seismic attenuation on seismic inversion as well as micro scale paleo geomorphology recognized by trend surface analysis. By comparing the distribution of karstification with paleo geomorphology, fault distribution and gas contents, it can be observed that the karstification have a good matching relationship with these factors. The dominant type of karstification is epigenic karst, which is strongly influenced by depositional facies and paleo geomorphology. Fracture networks have contributed to karstification but are not the dominant factor of karst formation. The integration of geological and geophysical methods can predict karst reservoir with high accuracy within large area and can be applied to karst reservoir hydrocarbon exploration of similar geological setting.

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