Study on origin, classification, and identification of complex porous carbonate reservoir: a case study of the middle cretaceous Mishrif Formation in W Oil field

被引:0
作者
Xinxin Fang
Hong Feng
Hao Wang
Yunhong Wang
机构
[1] China Coal Research Institute,
[2] Research Institute of Petroleum Exploration and Development,undefined
[3] China Coal Technology and Engineering Group Xi’an Institute,undefined
来源
Carbonates and Evaporites | 2021年 / 36卷
关键词
Reservoir; Carbonate; Rock type; Diagenesis; Mishrif;
D O I
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学科分类号
摘要
Based on data of core, thin section, well logging and capillary pressure curves, starting from rock and pore types, reservoir classification is carried out integrating macro and micro perspectives, which can comprehensively reflect the relationship between pore and permeability and micro-pore structure of reservoir and seepage ability. Studied area was subdivided into five kinds of reservoirs, i.e., Class I, Class II, Class III, and Class IV and Class V. Among them, quality of the Class I, dominated by rudstone, were the best with high-porosity and ultra-high-permeability. The quality of reservoirs gradually deteriorated from Class I to Class V. Studies revealed that the formation of reservoirs was affected by sequence stratigraphy, sedimentation, and diagenesis strongly. The high-water system tract and transgressive system tract form a vertical periodic distribution of reservoirs. Reef flats and open platform facies were more conducive to the formation of high-quality reservoirs, such as grainstone, while subtidal zone and lagoon facies were more favorable for the formation of mud supported structure, such as wackstone and mudstone. Late dissolution played major roles in the increase of secondary pores, such as vugs and moldic pores. Cementation, compaction and micritization reduced primary pore and reduced reservoir quality. The new reconstructed parameters RG (resistivity to natural gamma ray ratio, RG = ILD/GR) and RD (acoustic time difference to density ratio, RD = DT/ RHOB) could be used to accurately identify reservoir types. The results show that prediction accuracy of this method is up to 90%, which can meet the requirements of fine reservoir evaluation.
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