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Tytuł artykułu

Comparative study of models for predicting permeability from nuclear magnetic Rresonance (NMR) logs in two Chinese tight sandstone reservoirs

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Based on the analysis of mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) experimental data for core plugs, which were drilled from two Chinese tight sandstone reservoirs, permeability prediction models, such as the classical SDR, Timur– Coates, the Swanson parameter, the Capillary Parachor, the R10 and R35 models, are calibrated to estimating permeabilities from field NMR logs, and the applicabilities of these permeability prediction models are compared. The processing results of several field examples show that the SDR model is unavailable in tight sandstone reservoirs. The Timur– Coates model is effective once the optimal T2cutoff can be acquired to accurately calculate FFI and BVI from field NMR logs. The Swanson parameter model and the Capillary Parachor model are not always available in tight sandstone reservoirs. The R35 based model cannot effectively work in tight sandstone reservoirs, while the R10 based model is optimal in permeability prediction.
Czasopismo
Rocznik
Strony
116--141
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
autor
  • Key Laboratory of Geo-detection (China University of Geosciences), Ministry of Education, Beijing, China
  • School of Geophysics and Information Technology, China University of Geosciences, Beijing, China
autor
  • Geological Exploration and Development Research Institute Sichuan-Changqing Drilling and Exploration Engineering Co., Chengdu, China
autor
  • Key Laboratory of Geo-detection (China University of Geosciences), Ministry of Education, Beijing, China
  • School of Geophysics and Information Technology, China University of Geosciences, Beijing, China
autor
  • Geological Exploration and Development Research Institute Sichuan-Changqing Drilling and Exploration Engineering Co., Chengdu, China
autor
  • State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China
autor
  • Research Institute of Exploration and Development, Changqing Oilfield, PetroChina, Xi’an, China
autor
  • Research Institute of Exploration and Development, Changqing Oilfield, PetroChina, Xi’an, China
autor
  • Research Institute of Exploration and Development, Changqing Oilfield, PetroChina, Xi’an, China
Bibliografia
  • 1.Abbaszadeh, M., H. Fujii, and F. Fujimoto (1996), Permeability prediction by hydraulic flow units – theory and applications, SPE Formation Eval. 11, 4, 263-271, DOI: 10.2118/30158-PA.
  • 2.Adolfo, D.W. (2007), Reservoir zonation and permeability estimation: A Bayesian approach. In: SPWLA 48th Annual Logging Symposium, 3-6 June 2007, Austin, USA, Conference paper 2007-UUU.
  • 3.Al-Ajmi, F.A., and S. Holditch (2000), Permeability estimation using hydraulic flow units in a Central Arabia reservoir. In: SPE Ann. Tech. Conf. Exhibit., 1-4 October 2000, Dallas, USA, Conference paper 63254-MS, DOI: 10.2118/63254-MS.
  • 4.Bust, V.K., Oletu, J.U., and P.F. Worthington (2011), The challenges for carbonate petrophysics in petroleum resource estimation, SPE Reserv. Eval. Eng. 14, 1, 25-34, DOI: 10.2118/142819-PA.
  • 5.Coates, G.R., L.Z. Xiao, and M.G. Prammer (1999), NMR Logging: Principles and Applications, Haliburton Energy Services, Houston, 234 pp.
  • 6.Dunn, K.-J., D.J. Bergman, and G.A. Latorraca (2002), Nuclear Magnetic Resonance: Petrophysical and Logging Applications, Handbook of Geophysical Exploration, Vol. 32, Pergamon, New York, 176 pp.
  • 7.Guo, B.Y., A. Ghalambor, and S.K. Duan (2004), Correlation between sandstone permeability and capillary pressure curves, J. Petrol. Sci. Eng. 43, 3-4, 239- 246, DOI: 10.1016/j.petrol.2004.02.016.
  • 8.Haghighi, M.B., M. Shabaninejad, and K. Afsari (2011), A permeability predictive model based on hydraulic flow unit for one of Iranian carbonate tight gas reservoir. In: SPE Middle East Unconventional Gas Conference and Exhibition, 31 January – 2 February 2011, Muscat, Oman, Conference paper 142183-MS, DOI: 10.2118/142183-MS.
  • 9.Huet, C.H., J.A. Rushing, K.E. Newsham, and T.A. Blasingame (2007), Estimating Klinkenberg-corrected permeability from mercury-injection capillary pressure data: A new semianalytical model for tight gas sands. In: Rocky Mountain Oil & Gas Technology Symposium, 16-18 April 2007, Denver, USA, Conference paper 102890-MS, DOI: 10.2118/102890-MS.
  • 10.Jennings Jr., J.W., and F.J. Lucia (2003), Predicting permeability from well logs in carbonates with a link to geology for interwell permeability mapping, SPE Reserv. Eval. Eng. 6, 4, 215-225, DOI: 10.2118/84942-PA.
  • 11.Kenyon, W.E. (1997), Petrophysical principles of applications of NMR logging, The Log Analyst 38, 3, 21-43.
  • 12.Kenyon, W.E., P.I. Day, C. Straley, and J.F. Willemsen (1988), A three-part study of NMR longitudinal relaxation properties of water-saturated sandstones, SPE Formation Eval. 3, 3, 622-636, DOI: 10.2118/15643-PA.
  • 13.Kenyon, W.E., C. Straley, and C.F. Morriss (1991), NMR in partially saturated rocks: laboratory insights on free fluid index and comparison with borehole logs. In: SPWLA 32nd Annual Logging Symposium 1991, Conference paper 1991-CC.
  • 14.Kolodzie Jr., S. (1980), Analysis of pore throat size and use of the Waxman-Smits equation to determine OOIP in Spindle Field, Colorado. In: SPE Annual Technical Conference and Exhibition, 21-24 September 1980, Dallas, USA, Conference paper 9382-MS, DOI: 10.2118/9382-MS.
  • 15.Lafage, S.I. (2008), An alternative to the Winland R35 method for determining carbonate reservoir quality, M.Sc. Thesis, Texas A&M University, USA.
  • 16.Mao, Z.-Q., L. Xiao, Z.-N. Wang, Y. Jin, X.-G. Liu, and B. Xie (2013), Estimation of permeability by integrating nuclear magnetic resonance (NMR) logs with mercury injection capillary pressure (MICP) data in tight gas sands, Appl. Magn. Reson. 44, 4, 449-468, DOI: 10.1007/s00723-012-0384-z.
  • 17.Naeeni, M.N., H. Zargari, R. Ashena, R. Ashena, and R. Kharrat (2010), Permeability prediction of un-cored intervals using new IMLR method and artificial neural networks: A case study of Bangestan field, Iran. In: SPE – Nigeria Annual International Conference and Exhibition, 31 July – 7 August 2010, Tinapa–Calabar, Nigeria, 140682-MS, DOI: 10.2118/140682-MS.
  • 18.Pittman, E.D. (1992), Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for sandstone, Am. Assoc. Petrol. Geol. Bull. 76, 2, 191-198.
  • 19.Prasad, M. (2003), Velocity-permeability relations within hydraulic units, Geophysics 68, 1, 108-117, DOI: 10.1190/1.1543198.
  • 20.Purcell, W.R. (1949), Capillary pressures – their measurement using mercury and the calculation of permeability therefrom, Trans. AIME 186, 3, 39-48.
  • 21.Rezaee, R., A. Saeedi, and B. Clennell (2012), Tight gas sands permeability estimation from mercury injection capillary pressure and nuclear magnetic resonance data, J. Petrol. Sci. Eng. 88-89, 92-99, DOI: 10.1016/j.petrol. 2011.12.014.
  • 22.Shahvar, M.B., R. Kharrat, and M. Matin (2010), Applying flow zone index approach and artificial neural networks modeling technique for characterizing a heterogeneous carbonate reservoir using dynamic data: Case study of an Iranian reservoir. In: SPE – Trinidad and Tobago Energy Resources Conference, 27-30 June 2010, Port of Spain, Trinidad, Conference paper 132898-MS, DOI: 10.2118/132898-MS.
  • 23.Swanson, B.F. (1981), A simple correlation between permeabilities and mercury capillary pressures, J. Petrol. Technol. 33, 12, 2498-2504, DOI: 10.2118/ 8234-PA.
  • 24.Thomeer, J.H.M. (1960), Introduction of a pore geometrical factor defined by the capillary pressure curve, Trans. Am. Inst. Min. Met. Eng. 219, 12, 354-358.
  • 25.Xiao, L., Z.-Q. Mao, G.-R. Li, and Y. Jin (2012a), Calculation of porosity from nuclear magnetic resonance and conventional logs in gas-bearing reservoirs, Acta Geophys. 60, 4, 1030-1042, DOI: 10.2478/s11600-012-0015-y.
  • 26.Xiao, L., Z.-Q. Mao, Z.-N. Wang, and Y. Jin (2012b), Application of NMR logs in tight gas reservoirs for formation evaluation: A case study of Sichuan basin in China, J. Petrol. Sci. Eng. 81, 182-195, DOI: 10.1016/j.petrol. 2011. 12.025.
  • 27.Xiao, Z.X., L. Xiao, and W. Zhang (2008), A new method for calculating sandstone permeability by using capillary pressure curves, Geophys. Prospect. Petrol. 47, 2, 204-207 (in Chinese).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-185eefe2-5988-482c-bde1-af66d74bf2e7
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