The Magu 1 buried hill is located in the middle section of the western depression of the Liaohe Depression, mainly composed of Archean metamorphic rocks with developed fractured and porous reservoirs. The reservoir is deeply buried, the imaging accuracy of seismic data is low, and effective reservoir prediction is difficult, with strong versatility. By establishing qualitative identification modes and quantitative identification charts for the main lithology, the lithology identification standards were determined, and lithology quantitative identification was carried out. Five qualitative identification modes for logging rock classification were summarized, and the dominant lithology sequence was identified. Favorable reservoirs for single wells were classified. On the basis of comprehensive evaluation of reservoirs, a matching model of “favorable reservoirs sensitive seismic attributes single well productivity” was established by combining dynamic and static optimization of seismic attributes, effectively reducing the multi solution of single attribute prediction, determining the seismic attribute boundaries of favorable reservoirs, achieving quantitative prediction of effective reservoirs, and achieving good application results.

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Quantitative Evaluation and Prediction of Favorable Reservoirs in Magu 1 Buried Hill of Liaohe Oilfield

  • De-hua Li,
  • Xian-Xue Chen,
  • Xian-yan Feng,
  • Li-xun Sun,
  • Zi-ji Wang,
  • Tian-guang Zhang,
  • Yun-hao Guan,
  • Kai-yuan Zhang,
  • Hai-yan Liu

摘要

The Magu 1 buried hill is located in the middle section of the western depression of the Liaohe Depression, mainly composed of Archean metamorphic rocks with developed fractured and porous reservoirs. The reservoir is deeply buried, the imaging accuracy of seismic data is low, and effective reservoir prediction is difficult, with strong versatility. By establishing qualitative identification modes and quantitative identification charts for the main lithology, the lithology identification standards were determined, and lithology quantitative identification was carried out. Five qualitative identification modes for logging rock classification were summarized, and the dominant lithology sequence was identified. Favorable reservoirs for single wells were classified. On the basis of comprehensive evaluation of reservoirs, a matching model of “favorable reservoirs sensitive seismic attributes single well productivity” was established by combining dynamic and static optimization of seismic attributes, effectively reducing the multi solution of single attribute prediction, determining the seismic attribute boundaries of favorable reservoirs, achieving quantitative prediction of effective reservoirs, and achieving good application results.