DOI QR코드

DOI QR Code

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.

MRTSBC_2019_v22n1_1_f0001.png 이미지

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

MRTSBC_2019_v22n1_1_f0002.png 이미지

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

MRTSBC_2019_v22n1_1_f0003.png 이미지

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

MRTSBC_2019_v22n1_1_f0004.png 이미지

Fig. 4. Flowchart for joint inversion procedures.

MRTSBC_2019_v22n1_1_f0005.png 이미지

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

MRTSBC_2019_v22n1_1_f0006.png 이미지

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

MRTSBC_2019_v22n1_1_f0007.png 이미지

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

MRTSBC_2019_v22n1_1_f0008.png 이미지

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

MRTSBC_2019_v22n1_1_f0009.png 이미지

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

MRTSBC_2019_v22n1_1_f0010.png 이미지

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

MRTSBC_2019_v22n1_1_f0011.png 이미지

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

MRTSBC_2019_v22n1_1_f0012.png 이미지

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

MRTSBC_2019_v22n1_1_f0013.png 이미지

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

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

MRTSBC_2019_v22n1_1_t0001.png 이미지

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

MRTSBC_2019_v22n1_1_t0002.png 이미지

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

MRTSBC_2019_v22n1_1_t0003.png 이미지

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

MRTSBC_2019_v22n1_1_t0004.png 이미지

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

MRTSBC_2019_v22n1_1_t0005.png 이미지

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

MRTSBC_2019_v22n1_1_t0006.png 이미지

Acknowledgement

Supported by : 해양수산부, 한국에너지기술평가원(KETEP)

References

  1. Alberta Geological Survey, 2015, Alberta table of formations, Alberta Energy Regulator
  2. Asquith, G., and Krygowski, D., 2004, Basic well log analysis, 2nd Ed., Am. Assoc. Pe. Geol.
  3. Backus, G. E., and Gilbert, J. F., 1967, Numerical application of a formalism for geophysical inverse problems, Geophys. J. Int., 13(1-3), 247-276. https://doi.org/10.1111/j.1365-246X.1967.tb02159.x
  4. Calvert, T. J., Raw, R. N., and Wells, L. E., 1977, Electromagnetic propagation-A new dimension logging, Paper SPE 6542 presented at the Annual California Regional Meeting, Soc. of Pet. Eng., Bakersfield, California, April 13-15.
  5. Dell'Aversana, P., Bernasconi, G., Miotti, F., and Rovetta, D., 2011, Joint inversion of rock properties from sonic, resistivity and density well-log measurements, Geophys. Prospect., 59(6), 1144-1154. https://doi.org/10.1111/j.1365-2478.2011.00996.x
  6. Jeong, S., Seol, S. J., and Byun, J., 2015, Effective estimation of porosity and fluid saturation using joint inversion result of seismic and electromagnetic data. Geophys. and Geophys. Explor., 18(2), 54-63 (in Korean with English abstract). https://doi.org/10.7582/GGE.2015.18.2.054
  7. Jupp, D. L. B., and Vozoff, K., 1975, Stable iterative methods for the inversion of geophysical data, Geophys. J. Int., 42(3), 957-976.
  8. Kim, J. H., Yi, M. J., and Hwang, S. H., 2004, Automatic inversion of normal resistivity logs for horizontally stratified Earth, J. Korea Inst. Mineral Mining Eng., 41(4), 271-284 (in Korean with English abstract).
  9. Larionov, V. V., 1969, Borehole radiometry: Moscow, U.S.S.R., Nedra.
  10. Lee, D. G., Seo, K., and Lim, J. S., 2011, 3D Geostatical modeling using well data of oil sand reservoir in the Leismer Field, Canada, J. Korea Inst. Mineral Mining Eng., 48(6), 687-700 (in Korean with English abstract).
  11. Lee, G. H., 2016, Rock physics modeling: Report and a case study, Econ. Environ. Geol., 49(3), 225-242 (in Korean with English abstract). https://doi.org/10.9719/EEG.2016.49.3.225
  12. Lichtenecker, K., and Rother, K., 1931, Die herleitung des logarithmischen mischungs-gesetzes aus allegemeinen prinzipien der stationaren stromung, Phys. Z., 32, 255-260.
  13. Mavko, G., Mukerji, T., and Dvorkin, J., 1998, The rock physics handbook: Tools for seismic analysis of porous media, Cambridge University Press.
  14. Park, C., and Nam, M. J., 2014, A review on constructing seismic rock physics models based on gassmann's equation for reservoir fluid substitution, J. Korea Inst. Mineral Mining Eng., 51(3), 448-467 (in Korean with English abstract).
  15. Putnam, P. E., and Oliver, T. A., 1980, Stratigraphic traps in channel sandstones in the Upper Mannville (Albian) of eastcentral Alberta, B. Can. Petrol. Geol., 28(4), 489-508.
  16. Putnam, P. E., 1982, Aspects of the petroleum geology of the Lloydminster heavy oil fields, Alberta and Saskatchewan, B. Can. Petrol. Geol., 30(2), 81-111.
  17. Quijada, M. F., 2009, Estimating elastic properties of sandstone reservoirs using well logs and seismic inversion, MSc thesis, University of Calgary, 27p
  18. Raymer, L. L., Hunt, E. R., and Gardner, J. S., 1980, An improved sonic transit time to porosity transform, 21st Annual Logging Symposium, Transactions of the Society of Professinal Well Log Analysis, Expanded Abstract, 546p.
  19. Schon, J. H., 2015, Physical propeties of rocks: Fundamentals and principles of petrophysics, 2nd Ed., Elsevier.
  20. Smith, T. M., Sondergeld, C. H., and Rai C. S., 2003, Gassmann fluid substitutions: A tutorial., Geophysics., 68(2), 430-440. https://doi.org/10.1190/1.1567211
  21. Son, H. R., Lee, W. S., and Kim, H. T., 2007, Rock properties evaluation of tight gas reservoir using geophysical well logs in chimney butte field, wyoming, J. Korea Inst. Mineral Mining Eng., 44(5), 418-427 (in Korean with English abstract).
  22. Tarantola, A., 2005, Inverse problem theory., SIAM.