A New Model for Permeability Estimation In Carbonate Reservoirs By Using NMR T2 Distribution and Lsboost Ensemble Technique

Document Type : Research Paper


1 Department of Petroleum Engineering, Kish International Campus, University of Tehran, Kish, Iran\Petroleum Engineering Department, National Iranian Southfield Oil Company (NISOC), Ahvaz, Iran

2 Department of Petroleum Engineering, Kish International Campus, University of Tehran, Kish, IranInstitute of Petroleum Engineering, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran

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


Permeability is arguably the most critical property for evaluating flow in the reservoir. It is also one of the challenging parameters which must be measured in the field. Nuclear Magnetic Resonance (NMR) logging across the borehole is among the popular techniques, which it is utilized to determine permeability across the reservoir. However, available correlations in literature for estimating permeability from NMR data do not usually provide acceptable accuracy in the carbonate rocks. Therefore, a new model is proposed to estimate permeability by establishing a relationship between core derived permeability and extracted features from the T2 distribution curve of NMR data with the ensemble LSBoost algorithm. The feature extraction process is performed using peak analysis on T2 distribution curves which it leads to 5 relevant parameters, including T2lm, TCMR, prominence, peak amplitude and width. The proposed model is validated by comparing the proposed method’s correlation coefficient against Timur-Coates and SDR equation estimation accuracy. The results show that our model generally provides better prediction accuracies in comparison with the empirical equation-based derived permeabilities.


  1. Lucia F J (2007) Carbonate reservoir characterization: an integrated approach, 1st ed., Springer Science and Business Media, 1-336.##
  2. Dunn KJ, LaTorraca GA, Warner JL, Bergman DJ (1994) On the calculation and interpretation of NMR relaxation time distributions, In SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers. ##
  3. Jianwei D (2015) Permeability characterization and prediction in a tight oil reservoir, Edson Field, Alberta, PhD thesis, University of Calgary, 113. ##
  4. Cai J, Kai X, Yanhui Z, Fang H, Liuhuan L (2020) Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest, Applied energy, 262: 114566. ##
  5. Aghda, S. 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 of Petroleum Exploration and Production Technology, 8, 4: 1113-1127. ##
  6. Bordenave ML, Hegre JA (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. ##
  7. Sepehr M, Cosgrove JW (2004) Structural framework of the Zagros fold–thrust belt, Iran, Marine and petroleum geology, 21, 7: 829-843. ##
  8. Homke S, Jaume V, Miguel G, Hadi E, Ridvan K (2004) Magnetostratigraphy of Miocene–Pliocene Zagros foreland deposits in the front of the Push-e Kush arc, Lurestan Province, Iran, Earth and Planetary Science Letters, 225, 3: 397-410.
  9. Motiei H (1993) Stratigraphy of Zagros, Treatise on the Geology of Iran, 60: 151. ##
  10. Alsharhan AS, Nairn AEM (1993) Carbonate platform models of Arabian Cretaceous reservoirs, 173-184. ##
  11. Schroeder R, Frans SP van B, Antonietta C, Darioush B, Benoit V, Adrian I, Bruno G (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. ##
  12. 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. ##
  13. MMaurer F, Van Buchem FS, Eberli GP, Pierson BJ, Raven MJ, Larsen PH, Al-Husseini MI, Vincent B (2013) Late Aptian long-lived glacio-eustatic lowstand recorded on the Arabian Plate. Terra Nova, 25, 2: 87-94. ##
  14. 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. ##
  15. Coates GR, Xiao LI ZHI, Prammer MG (1999) NMR logging. Principles and Applications, 1st ed., Halliburton Energy Services Publication, 1-227. ##
  16. Lis-Śledziona A (2019) Petrophysical rock typing and permeability prediction in tight sandstone reservoir, Acta Geophysica, 67, 6: 1895-1911. ##
  17. Lu Z, Sha A, Wang W (2020) Permeability evaluation of clay-quartz mixtures based on low-field NMR and fractal analysis, Applied Sciences, 10, 5: 1585. ##
  18. Timur A (1968) An investigation of permeability, porosity, and residual water saturation relationships. In: SPWLA 9th annual logging symposium, New Orleans, Louisiana, Society of Petrophysicists and Well-Log Analysts. ##
  19. Coates G R, Denoo S (1988) The producibility answer product, Schlumberger Technical Review, 29, 2: 55. ##
  20. Kenyon WE, Day PI, Straley C, Willemsen J (1986)  Compact and consistent representation of rock nmr  data for permeability estimation, paper SPE 15643. ##
  21. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm, In Proceedings of the Thirteenth International Conference on International Conference on Machine Learning (ICML), Bari, Italy, 3–6: 148–156. ##
  22. Schapire RE (2003) The boosting approach to machine learning: an overview nonlinear estimation and classification, Springer: New York, NY, USA, 149–171. ##
  23. Bishop CM (2007) Pattern recognition and machine learning, Springer: New York, NY, USA, 1–738. ##
  24. Su M, Zhang Z, Zhu Y, Zha D (2019) Data-driven natural gas spot price forecasting with least squares regression boosting algorithm, Energies, 12, 6: 1094; https://doi.org/10.3390/en12061094. ##
  25. Jung C, Schindler D (2015) Statistical modeling of near-surface wind speed: a case study from Baden-Wuerttemberg (Southwest Germany), Austin Journal of Earth Science2, 1: 1006. ##
  26. Cherkassky V, Ma Y (2009) Another look at statistical learning theory and regularization. Neural Networks, 22, 7: 958-969. ##
  27. Bianco V Manca O, Nardini S (2009) Electricity consumption forecasting in Italy using linear regression models, Energy, 34: 1413–1421. ##
  28. Breiman L (2001) Random forests, Machine Learning, 45, 5–32. ##
  29. Dargahi Zarandia A, Hemmati Sarapardehb A, Shateric M, Menad NA, Ahmadi M (2020) A modeling minimum miscibility pressure of pure/impure CO2-crude oil systems using adaptive boosting support vector regression: Application to gas injection processes, Journal of Petroleum Science and Engineering, 184: 106499, https://doi.org/10.1016/j.petrol.2019.106499. ##
  30. Wang FK, Mamo T (2020) Gradient boosted regression model for the degradation analysis of prismatic cells, Computers and Industrial Engineering, doi:https://doi.org/10.1016/j.cie.2020.106494. ##
  31. Li X, Zhang L, Wang Z, Dong P (2019) Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks, Journal of Energy Storage, 21, 510-518. ##