Skip to main content
Log in

Calculation of porosity from nuclear magnetic resonance and conventional logs in gas-bearing reservoirs

  • Research Article
  • Published:
Acta Geophysica Aims and scope Submit manuscript

Abstract

The porosity may be overestimated or underestimated when calculated from conventional logs and also underestimated when derived from nuclear magnetic resonance (NMR) logs due to the effect of the lower hydrogen index of natural gas in gas-bearing sandstones. Proceeding from the basic principle of NMR log and the results obtained from a physical rock volume model constructed on the basis of interval transit time logs, a technique of calculating porosity by combining the NMR log with the conventional interval transit time log is proposed. For wells with the NMR log acquired from the MRIL-C tool, this technique is reliable for evaluating the effect of natural gas and obtaining accurate porosity in any borehole. In wells with NMR log acquired from the CMR-Plus tool and with collapsed borehole, the NMR porosity should be first corrected by using the deep lateral resistivity log. Two field examples of tight gas sandstones in the Xujiahe Formation, central Sichuan basin, Southwest China, illustrate that the porosity calculated by using this technique matches the core analyzed results very well. Another field example of conventional gas-bearing reservoir in the Ziniquanzi Formation, southern Junggar basin, Northwest China, verifies that this technique is usable not only in tight gas sandstones, but also in any gas-bearing reservoirs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Abushanab, M.M., G.M. Hamada, A.A. Abdelwally, and M.E. Oraby (2005), DMR technique improves tight gas porosity estimate, Oil Gas J. 103,47, 54–59.

    Google Scholar 

  • Archie, G.R. (1942), The electrical resistivity log as an aid in determining some reservoir characteristics, Trans. AIME, 146, 54–62.

    Google Scholar 

  • Chu, Z.H., J. Gao, L.J. Huang, and L.Z. Xiao (2007), Principles and methods of geophysical logging (Part II), Petroleum Industry Pressure, Beijing, 224–326.

  • Clavier, C., G. Coates, and J. Dumanoir (1984), Theoretical and experimental bases for the dual-water model for interpretation of shaly sands, SPE J. 24,2, 153–168, DOI: 10.2118/6859-PA.

    Google Scholar 

  • Coates, G.R., L.Z. Xiao, and M.G. Prammer (2000), NMR Logging Principles and Applications, Gulf Publishing Company, Houston, 42–78.

    Google Scholar 

  • Hamada, G.M., and M.A. Abushanab (2007), Better porosity estimate of gas sandstone reservoir using density and NMR logging data, SPE Conf. Paper 106627, DOI: 10.2118/106627-MS.

  • Kamel, M.H., and M.M. Mohamed (2006), Effective porosity determination in clean/shaly formations from acoustic logs with applications, J. Petrol. Sci. Eng. 51,3, 267–274, 10.1016/j.petrol.2006.01.007.

    Article  Google Scholar 

  • Kamel, M.H., W.M. Mabrouk, and A.I. Bayoumi (2002), Porosity estimation using a combination of Wyllie-Clemenceau equation in clean sand formation from acoustic logs, J. Petrol. Sci. Eng. 33,4, 241–251, DOI: 10.1016/S0920-4105(01)00169-3.

    Article  Google Scholar 

  • Makar, K.H., and M.H. Kamel (2011), An approach for minimizing errors in computing effective porosity in reservoir of shaly nature in view of Wyllie-Raymer-Raiga relationship, J. Petrol. Sci. Eng. 77,3, 386–392, DOI: 10.1016/jpetrol.2011.04.013.

    Article  Google Scholar 

  • Mao, Z.Q., C. Zhang, and L. Xiao (2010), A NMR-based porosity calculation method for low porosity and low permeability gas reservoir, Oil Geophys. Prospect. 45,1, 105–109 (in Chinese).

    Google Scholar 

  • Raiga-Clemenceau, J., J.P. Martine, and S. Nicoletis (1988), The concept of acoustic formation factor for more accurate porosity determination from sonic transit time data, The Log Analyst. 29,1, 54–60

    Google Scholar 

  • Raymer, L.L., E.R. Hunt, and J.S. Gardner (1980), An improved sonic transit timeto-porosity transform, SPWLA 21st Annual Logging Symp., Conf. Paper 1980-P.

  • Waxman, M.H. (1974), Electrical conductivities in shaly Sands—I. The relation between hydrocarbon saturation and resistivity index; II. The temperature coefficient of electrical conductivity, J. Petrol. Technol. 26,2, 213–225, DOI: 10.2118/4094-PA.

    Google Scholar 

  • Waxman, M.H., and L.J.M. Smits (1968). Ionic double-layer conductivity in oilbearing shaly sands, SPE Formation Eval. 4,1, 20–32.

    Google Scholar 

  • Wyllie, M.R.J., A.R. Gregory, and L.W. Gardner (1956), Elastic wave velocities in heterogeneous and porous media, Geophysics. 21,1, 41–70, DOI: 10.1190/1.1438217.

    Google Scholar 

  • Xiao, L.Z. (1998), Magnetic Resonance Imaging Logging and Rock Nuclear Magnetic Resonance and its Application, Science Press, Beijing (in Chinese).

    Google Scholar 

  • Yong, S.H., C.M. Zhang, and Z.Y. Liu (1996), Well Log Data Processing and Comprehensive Interpretation, China University of Petroleum Press, Dongyin (in Chinese).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Liang Xiao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xiao, L., Mao, Zq., Li, Gr. et al. Calculation of porosity from nuclear magnetic resonance and conventional logs in gas-bearing reservoirs. Acta Geophys. 60, 1030–1042 (2012). https://doi.org/10.2478/s11600-012-0015-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s11600-012-0015-y

Key words

Navigation