Geophysics and Geophysical Exploration (지구물리와물리탐사)

Korean Society of Earth and Exploration Geophysicists (한국지구물리·물리탐사학회)
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Formation Estimation of Shaly Sandstone Reservoir using Joint Inversion from Well Logging Data

복합역산을 이용한 물리검층자료로부터의 셰일성 사암 저류층의 지층 평가

  • Choi, Yeonjin (Korea Maritime and Ocean University, Department of Energy & Resources Engineering) ;
  • Chung, Woo-Keen (Korea Maritime and Ocean University, Department of Energy & Resources Engineering) ;
  • Ha, Jiho (Korea Institude of Geoscience and Mineral Resources (KIGAM) Pohang Branch) ;
  • Shin, Sung-ryul (Korea Maritime and Ocean University, Department of Energy & Resources Engineering)
  • 최연진 (한국해양대학교 에너지자원공학과) ;
  • 정우근 (한국해양대학교 에너지자원공학과) ;
  • 하지호 (한국지질자원연구원 포항지질자원실증연구센터) ;
  • 신성렬 (한국해양대학교 에너지자원공학과)
  • Received : 2018.08.03
  • Accepted : 2019.02.28
  • Published : 2019.02.28

Abstract

Well logging technologies are used to measure the physical properties of reservoirs through boreholes. These technologies have been utilized to understand reservoir characteristics, such as porosity, fluid saturation, etc., using equations based on rock physics models. The analysis of well logs is performed by selecting a reliable rock physics model adequate for reservoir conditions or characteristics, comparing the results using the Archie's equation or simandoux method, and determining the most feasible reservoir properties. In this study, we developed a joint inversion algorithm to estimate physical properties in shaly sandstone reservoirs based on the pre-existing algorithm for sandstone reservoirs. For this purpose, we proposed a rock physics model with respect to shale volume, constructed the Jacobian matrix, and performed the sensitivity analysis for understanding the relationship between well-logging data and rock properties. The joint inversion algorithm was implemented by adopting the least-squares method using probabilistic approach. The developed algorithm was applied to the well-logging data obtained from the Colony gas sandstone reservoir. The results were compared with the simandox method and the joint inversion algorithms of sand stone reservoirs.

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Fig. 1. Rock physics modeling of P-wave velocity for (a) cleans and, (b) shaly sand reservoir model.

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Fig. 2. Rock physics modeling of conductivity for (a) (a) clean sand and (b) shaly sand reservoir models.

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Fig. 3. Rock physics modeling of density for (a) clean sand and(b) shaly sand reservoir models.

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Fig. 4. Flowchart for joint inversion procedures.

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Fig. 5. Absolute values of eigenvectors in the model space.

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Fig. 6. Stratigraphic summary of the Colony sand units (modified after Alberta Geological Survey (2015)).

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Fig. 7. Well log data of Colony gas sand.

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Fig. 8. Estimation of reservoir properties using the simandoux method.

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Fig. 9. Measured and calculated data for the joint inversion in the shaly sand reservoir model.

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Fig. 10. Estimation of reservoir properties using the joint inversion in the shaly sand reservoir model.

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Fig. 11. Measured and calculated data for joint inversion in sand reservoir model.

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Fig. 12. Estimation of reservoir properties using the joint inversion in the sand reservoir model.

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Fig. 13. Estimation of reservoir properties for the Colony gas sand.

Table 1. Rock and fluid parameters in a reservoir for rock physics modeling.

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Table 2. Average reservoir properties at each zone using the simandoux method.

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Table 3. Rock and fluid parameters for the joint inversion in the shaly sand reservoir.

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Table 4. Average reservoir properties at each zone using the joint inversion in the shaly sand reservoir.

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Table 5. Average reservoir properties at each zone using the joint inversion in the sand reservoir.

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Table 6. Average porosities and water saturations at each zone.

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Acknowledgement

Supported by : 해양수산부, 한국에너지기술평가원(KETEP)

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