Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Does mudcake change the results of modeling gamma-gamma well-logging?

  • Rasouli, Fatemeh S. (Department of Physics, K.N. Toosi University of Technology)
  • Received : 2021.08.19
  • Accepted : 2022.03.21
  • Published : 2022.09.25
Download PDF
⟨ Previous Next ⟩

Abstract

Among the different techniques available, nuclear methods, including gamma-gamma logging tools, are of special importance. Though the real environment which surrounds the drilled borehole is a complex fractured medium which the fluid can flow through the porosities, simulation studies generally use the traditional model of a homogeneous mixture of formation and the liquid. Considering a previously published study, which shows that modeling of fluid flow in fractured reservoirs and simulating the formation as an inhomogeneous fractured medium leads to different results compared with those of homogeneous mixture, here we study the effect of the presence of drilling fluid (mudcake) on the response of the detectors in both the models. To study this effect, a typical gamma-gamma logging tool was modeled by using the MCNPX Monte Carlo code. The results show that the responses of the detectors in the mixture model in the presence of various thicknesses of mudcake are sensitive to the density of the formation material. However, this effect is not notable in the inhomogeneous fractured medium. These results emphasize the importance of the model employed for simulation of the medium in gamma-gamma well-logging.

Keywords

References

  1. D.V. Ellis, J.M. Singer, Well Logging for Earth Scientists, vol. 692, Springer, Dordrecht, 2007.
  2. I.M. Al Alfy, Mathematical derivation of density log from total gamma ray and neutron logs in clastic rocks, a case study, Egypt, Appl. Radiat. Isot. 142 (2018) 42-45. https://doi.org/10.1016/j.apradiso.2018.09.003
  3. F. Li, R.P. Gardner, Semi-empirical modeling of gamma-ray density logs with the possibility of obtaining more information, Appl. Radiat. Isot. 68 (4-5) (2010) 936-940. https://doi.org/10.1016/j.apradiso.2009.10.047
  4. R. Desbrandes, Encyclopedia of Well Logging, Editions OPHRYS, 1985.
  5. J. Liu, H. Wu, F. Zhang, S. Liu, Z.. Liu, H. Yan, Improvement in the method for borehole caliper measurement based on azimuthal gamma-gamma density well logging, Appl. Radiat. Isot. 145 (2019) 68-72. https://doi.org/10.1016/j.apradiso.2018.12.014
  6. M.I. Pinilla, A. Hellinger, L.K. Vo, W.L. Dunn, W. McNeil, A. Bahadori, Design studies using MCNP6® for an oil well logging prototype tool and a test facility, Radiat. Phys. Chem. 167 (2020), 108393. https://doi.org/10.1016/j.radphyschem.2019.108393
  7. W.S. Wu, L.J. Huang, Monte Carlo simulating of three detector density logging, Chin. J. Geophys. 47 (1) (2004) 181-187. https://doi.org/10.1002/cjg2.470
  8. H. Wu, F. Zhang, H. Guo, Y.. Xin, Z. Han, Impact of focused gamma ray beam angle on the response of density logging tool, Appl. Radiat. Isot. 123 (2017) 102-108. https://doi.org/10.1016/j.apradiso.2017.02.043
  9. W.A. Metwally, Porosity calculations using a C/O logging tool with boron-lined NaI detectors, Appl. Radiat. Isot. 69 (1) (2011) 217-219. https://doi.org/10.1016/j.apradiso.2010.08.003
  10. P. John, Modelling a simple gamma-gamma density well logging tool, Appl. Radiat. Isot. 48 (6) (1997) 843-853. https://doi.org/10.1016/S0969-8043(96)00307-7
  11. F.S. Rasouli, S.F. Masoudi, Effect of modeling porous media on the response of gamma-gamma well-logging tool, Sci. Rep. 10 (1) (2020) 1-10. https://doi.org/10.1038/s41598-019-56847-4