地质科技通报 (Jan 2023)

Quantitative well logging evaluation of diagenetic facies of deep and ultra deep tight sandstone reservoirs: A case study of Bozi-Dabei area in Kuqa Depression

  • Hongkun Liu,
  • Yong Ai,
  • Guiwen Wang,
  • Kangjun Chen,
  • Deyang Cai,
  • Juntao Cao,
  • Yuqiang Xie,
  • Dong Li,
  • Jin Lai

DOI
https://doi.org/10.19509/j.cnki.dzkq.2022.0256
Journal volume & issue
Vol. 42, no. 1
pp. 299 – 310

Abstract

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Bashijiqike Formation in Bozi-Dabei area develops deep and ultra deep tight sandstone reservoirs. It is urgent to reveal the main controlling factors of reservoir quality and establish a matching well logging evaluation method for high quality reservoirs. In order to quantitatively evaluate the diagenetic facies, based on the data of core, thin section, cast thin section and scanning electron microscope and etc., the petrology, physical properties, diagenesis types and intensity, diagenetic minerals and other characteristics of the tight reservoir of Bashijiqike Formation in Bozi-Dabei area were studied. Based on quantitative calculation of ompaction rate, cementation rate, dissolution pore content and comprehensive diagenetic coefficient, the diagenetic facies were classified according to characteristics of diagenesis intensity and its combination. The reservoirs can be divided into three types of diagenetic facies, i.e., medium compaction weak dissolution facies, carbonate cemented facies and compaction dense facies. Through the fitting analysis of thin section data and well logging data, the well logging calculation model of diagenetic comprehensive coefficient Cg was established, and the method of quantitative identification of reservoir diagenetic facies were established. The well logging data of Well X1, Well Y1 and etc were processed.The reliability of the model was verified by matching the identification results with physical property analysis and casting thin section. The establishment of well logging quantitative characterization method of diagenetic facies can provide guidance for "sweet spot" prediction.

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