Cluster Analysis to Use a New Method for Permeability Estimation in Carbonate Reservoirs by Using NMR T2 Distribution Parameters in the South of Iran

Document Type : Research Paper


1 Department of Petroleum Engineering, Kish International Campus, University of Tehran, IranDepartment of Petroleum Engineering, University of Tehran, Tehran, Iran

2 Department of Petroleum Engineering, Kish International Campus, University of Tehran, IranDepartment of Petroleum Engineering, University of Tehran, Tehran, Iran

3 Earth Sciences Department, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran


In particular, quantitative laboratory measurements are challenging to perform due to their costs and time consumption. So, the need to explore other available data interconnectivity to permeability is of great importance. One of these data is NMR (Nuclear Magnetic Resonance) log data which have been used frequently in recent studies. It is considered to segregate different groups, which can be obtained through cluster analysis. Using reliable parameters in the cluster analysis helps to segregate different rock units which can be used in the permeability models. To select reliable parameters, cross plots of the permeability versus extracted features from the NMR T2 distribution curve were plotted. Results indicate that TCMR, peak reading amplitudes, and T2Lm are the best permeability indicators, respectively. Cluster analysis was performed on T2LM, TCMR and Peak reading amplitudes, which showed the highest consistency with core derived data compared with other parameters. The crucial step is to determine the best estimate of the number of clusters. It is usually taken as a prior in most clustering algorithms. In this research, Davies-Bouldin criterion values versus the number of clusters were used to obtain the optimal number of clusters. The knee method, which finds the “knee” in many clusters vs. clustering evaluation graph, was used. A clustering model with the number of clusters from 2-100 was created. It showed the five is an optimal number of clusters. Subsequently, the Schlumberger-Doll-Research (SDR) coefficients for each cluster were modified using a curve fitting tool in the Matlab software. Results indicated that calculated permeability using cluster analysis showed a higher correlation by core derived permeability than the original SDR permeability model. Since this is the core part of the group attempt to use extracted T2 distribution features in permeability estimation in carbonate reservoirs, more investigation is required to attempt satisfactory results to standardize the value of the coefficient of the permeability models in carbonate rocks with different petrophysical properties.


  1. Lucia F J (2007) Carbonate reservoir characterization: an integrated approach, Springer Science and Business Media. ##
  2. Wei D F, Liu X P, Hu X X, Xu R, Zhu L (2015) Estimation of permeability from NMR logs based on formation classification method in tight gas sands, Acta Geophys, 63: 1316–1338. ##
  3. Ge, Xinmin, Fan Y, Liu J, Zhang L, Han Y, Xing D (2017) An improved method for permeability estimation of the bioclastic limestone reservoir based on NMR data, Journal of Magnetic Resonance, 283: 96-109. ##
  4. Aghda S M F, Taslimi M, Fahimifar A (2018) Adjusting porosity and permeability estimation by nuclear magnetic resonance: a case study from a carbonate reservoir of south of Iran, Journal Petrol Explor Prod Technol, 8: 1113–1127. ##
  5. Dunn K J, LaTorraca G A, Warner J L, Bergman D J (1994) On the calculation and interpretation of NMR relaxation time distributions, In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. ##
  6. Jianwei D (2015) Permeability characterization and prediction in a tight oil reservoir” Edson Field, Alberta, PhD thesis, University of Calgary, 113. ##
  7. Kaufman J (1994) Numerical models of fluid flow in carbonate platforms: implications for dolomitization. J Sediment Res 64, 1: 128–139. ##
  8. Lucia FJ (1995) Rock-fabric/petrophysical classificationof carbonate pore space for reservoir characterization. AAPG Bull 79, 9: 1275–1300. ##
  9. [9] Amabeoku MO et al (2001) Calibration of permeability derived from NMR Logs in carbonate reservoirs. SPE Middle East Oil Show, Society of Petroleum Engineers. ##
  10. Westphal H et al (2005) NMR measurements in carbonate rocks: problems and an approach to a solution. Pure Appl Geophys 162, 3:549–570. ##
  11. Alsharhan A S, Nairn A E M (1993) Carbonate platform models of Arabian Cretaceous reservoirs, 173-184. ##
  12. Schroeder R, van Buchem F S, Cherchi A, Baghbani D, Vincent B, Immenhauser A, Granier B (2010) Revised orbitolinid biostratigraphic zonation for the Barremian–Aptian of the eastern Arabian Plate and implications for regional stratigraphic correlations, GeoArabia Special Publication 4, 1: 49-96. ##
  13. Droste H (2010) High-resolution seismic stratigraphy of the Shu’aiba and Natih formations in the Sultanate of Oman: implications for Cretaceous epeiric carbonate platform systems, Geological Society, London, Special Publications, 329, 1: 145-162. ##
  14. Maurer F, Van Buchem F S, Eberli G P, Pierson B J, Raven M J, Larsen P H, Al-Husseini M I, Vincent B, (2013) Late Aptian long-lived glacio-eustatic lowstand recorded on the Arabian Plate, Terra Nova, 25, 2: 87-94. ##
  15. Mehrabi H, Rhimpour-Bonab H, Hajikazemi E, Esrafili-Dizaji B (2015) Geological reservoir characterization of the lower cretaceous dariyan formation, (Shu’aiba equivalent) in the Persian Gulf, Southern Iran, Marine and Petroleum Geology, 68: 132-157. ##
  16. Mehrabi H, Ranjbar-Karami R, Roshani-Nejad M (2019) Reservoir rock typing and zonation in sequence stratigraphic framework of the cretaceous dariyan formation, Persian Gulf, Carbonates and Evaporites, 34, 4: 1833-1853. ##
  17. Kenyon W J T L A (1997) Petrophysical principles of applications of NMR logging, The Log Analyst, 38. ##
  18. Kenyon P, Day C, Straley J, Willemsen A (1988) Three-part study of NMR longitudinal relaxation properties of water-saturated sandstones, SPE Formation Evaluation, 3: 622-636. ##
  19. Coates G R, Xiao R, Prammer M G (1999) NMR logging principles and applications, Halliburton Energy Services, Houston. ##
  20. Mao Z Q, Xiao L, Wang Z N, Jin Y, Liu X G, Xie B (2013) Estimation of permeability by integrating nuclear magnetic resonance (NMR) logs with mercury injection capillary pressure (MICP) data in tight gas sands, Applied Magnetic Resonance, 44, 4: 449-468. ##
  21. Bordenave M L, J A Hegre (2010) Current distribution of oil and gas fields in the Zagros Fold Belt of Iran and contiguous offshore as the result of the petroleum systems, Geological Society, London, Special Publications 330, 1: 291-353. ##
  22. Sepehr M, Cosgrove J W (2004) Structural framework of the Zagros fold–thrust belt, Iran, Marine and Petroleum geology, 21, 7: 829-843. ##