Reservoir Engineering

Application of Pre-stack Inversion Technology to Reservoir Prediction of Mahu Oilfield

  • Lian Guihui ,
  • Zhu Yating ,
  • Wang Xiaoguang ,
  • Li Zhou ,
  • Qin Ming
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  • PetroChina Xinjiang Oilfield Company, Karamay, Xinjiang 834000, China

Received date: 2021-03-20

  Revised date: 2021-11-22

  Online published: 2023-01-10

Abstract

To address the problem of low accuracy in identifying effective reservoirs in Mahu Sag, Baikouquan Formation in Well Block M2, Mahu Oilfield was taken as the study object. The geostatistical methods in combination with simultaneous and stochastic inversion were employed to predict effective reservoirs of dense glutenite in Mahu Oilfield, complete the reservoir quality and sweet spot classification evaluation, and characterize the reservoir characteristics of Baikouquan Formation, Mahu Oilfield in detail. The results of the study showed that S-wave prototype was established, and waveform factor parameters, quality control of inversion results and multi-attribute volume operations were adjusted, which not only solved the problem of wave impedance overlap in dense reservoirs, but also substantially improved the longitudinal and transverse wave resolution in reservoir prediction, and the objectivity and reliability of the inversion results were further determined. In addition, pre-stack inversion technology was used to improve the lithology probability and the prediction effect of reservoir physical properties, and successfully characterize the sand body boundary, providing reliable spatial constraints for geological and rock mechanics parameter modeling. The study results provide technical support for planning and deployment of reservoir evaluation and development in Baikouquan Formation, Mahu Oilfield.

Cite this article

Lian Guihui , Zhu Yating , Wang Xiaoguang , Li Zhou , Qin Ming . Application of Pre-stack Inversion Technology to Reservoir Prediction of Mahu Oilfield[J]. Special Oil & Gas Reservoirs, 2022 , 29(1) : 80 -84 . DOI: 10.3969/j.issn.1006-6535.2022.01.012

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