Quantitative evaluation of low-permeability gas reservoirs based on an improved fuzzy-gray method

  • Peng ZhuEmail author
  • Zhaoqun Zhu
  • Yuanyuan Zhang
  • Lianpu Sun
  • Yixin Dong
  • Zhiqiang Li
  • Ming Chen
Review Paper


Accurate and quantitative evaluation of low-permeability reservoirs and other more complex reservoirs not only depends on various control factors related to the reservoir but is also affected by our incomplete understanding of reservoirs and the imperfections inherent in evaluation methods. The evaluation of reservoirs may be improved by determining the integrity and uncertainty of the reservoir evaluation process and effectively examining the differences between evaluation results. Taking membership as the linking factor, utilizing the idea of information superposition, and combining the fuzzy comprehensive and gray relational evaluation methodologies in the uncertainty evaluation, this study presents a differentiated and improved internal algorithm and establishes a more effective fuzzy-gray comprehensive evaluation method. The example of E32 interval evaluation in the southern Nanbaxian gas field of the Qaidam Basin shows that this method not only considers the fuzziness and grayness related to reservoir evaluation but also provides improved discrimination of low-permeability reservoirs and increased accuracy in the reservoir description. According to the improved reservoir classification results, the E32 intervals are divided into four types of reservoirs, of which types I and II are favorable reservoirs. The lateral distribution of the reservoir is described in more detail than previously, with near-continuous knowledge, and there is a good distinction between the reservoir type. The obtained results are in line with the development rules of geological deposition and the geological knowledge base, approximately matching existing practical results and geological knowledge. This method can be used to quickly and effectively guide exploration and development strategies related to gas fields, and provides a new approach for low permeability and tight reservoir evaluation.


Reservoir evaluation Fuzzy mathematics Gray theory Low-permeability gas reservoirs Nanbaxian gas field 


  1. Anifowose F, Abdulraheem A (2011) Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization. J Nat Gas Sci Eng 3(3):505–517CrossRefGoogle Scholar
  2. Blockley DI, Foley L, Ball L, Davis JP (1997) Fuzziness, incompleteness and randomness: classification of uncertainty in reservoir appraisal. Pet Geosci 3(3):203–209CrossRefGoogle Scholar
  3. Chen P, Wei M (2015) Study on combination of some methods to determinate the weight based on grey relational analysis control and decision conference. IEEE 2015:2220–2226Google Scholar
  4. Dai J, Ye X, Tang L, Jin Z, Shao W, Hu Y, Zhang B (2003) Tectonic units and oil-gas potential of the Qaidam Basin. Chin J Geol 38(3):413–424Google Scholar
  5. Deng J (1993) Grey system. Huazhong university of science and technology press, p 23–44Google Scholar
  6. Ghadami N, Rasaei MR, Hejri S, Sajedian A, Afsari K (2015) Consistent porosity–permeability modeling, reservoir rock typing and hydraulic flow unitization in a giant carbonate reservoir. J Pet Sci Eng 131:58–69CrossRefGoogle Scholar
  7. Hou R, Liu Z (2012) Reservoir evaluation and development strategies of Daniudi tight sand gas field in the Ordos Basin. Oil Gas Geol 33(1):118–128Google Scholar
  8. Huang J (2008) Combining entropy weight and TOPSIS method for information system selection. IEEE Int Conf Autom Logist 2008:1281–1284Google Scholar
  9. Lai J, Wang G, Zheng X, Zhou L, Han C, Wu D, Huang L, Luo G (2015) Quantitative evaluation of tight gas sandstone reservoirs of Bashijiqike formation in Dabei gas field. J Cent South Univ 46(6):2285–2298Google Scholar
  10. Lai J, Wang G, Fan Z, Chen J, Wang S, Zhou Z, Fan X (2016) Insight into the pore structure of tight sandstones using NMR and HPMI measurements. Energy Fuel 30(12):10200–10214CrossRefGoogle Scholar
  11. Li B (2007) Fuzzy mathematics and its application. Hefei University of Technology Press, Hefei, pp 77–84Google Scholar
  12. Liang T, Chang Y, Guo X, Liu B, Wu J (2013) Influence factors of single well's productivity in the Bakken tight oil reservoir, Williston Basin. Pet Explor Dev 40(3):383–388CrossRefGoogle Scholar
  13. Liu Y M, Liang J, Qian J X (2004) The application of dynamic principal component analysis to enhance chunk monitoring of an industrial fluidized-bed reactor. Intelligent control and automation. WCICA 2004. Fifth world congress on. IEEE, pp 1685–1688Google Scholar
  14. Liu Y, Xia B, Liu X (2015) A novel method of orienting hydraulic fractures in coal mines and its mechanism of intensified conduction. J Nat Gas Sci Eng 27:190–199CrossRefGoogle Scholar
  15. Olatunji SO, Selamat A, Abdulraheem A (2014) A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction. Inform fusion 16:29–45CrossRefGoogle Scholar
  16. Ou C, Rui R, Li C, Yong H (2016) Multi-index and two-level evaluation of shale gas reserve quality. J Nat Gas Sci Eng 35:1139–1145CrossRefGoogle Scholar
  17. Ranjbar-Karami R, Shiri M (2014) A modified fuzzy inference system for estimation of the static rock elastic properties: a case study from the Kangan and Dalan gas reservoirs, south pars gas field, the Persian Gulf. J Nat Gas Sci Eng 21:962–976CrossRefGoogle Scholar
  18. Rostirolla SP, Mattana AC, Bartoszeck MK (2003) Bayesian assessment of favorability for oil and gas prospects over the Reconcavo basin, Brazil. AAPG Bull 87(4):647–666CrossRefGoogle Scholar
  19. Rui Z, Lu J, Zhang Z, Guo R, Ling K, Zhang R, Patil S (2017) A quantitative oil and gas reservoir evaluation system for development. J Nat Gas Sci Eng 42:31–39CrossRefGoogle Scholar
  20. Shao L, Li M, Li Y, Zhang Y, Lu J, Zhang W, Tian Z, Wen H (2014) Geological characteristics and controlling factors of shale gas in the Jurassic of the northern Qaidam Basin. Earth Sci Front 21(4):311–322Google Scholar
  21. Shi X, Shi G, Zhang Q (2002) Comparison of integrated decision results between fuzzy mathematics and grey theory. Pet Explor Dev 29(2):84–80Google Scholar
  22. Tamiloli N, Venkatesan J, Ramnath BV (2016) A grey-fuzzy modeling for evaluating surface roughness and material removal rate of coated end milling insert. Measurement 84:68–82CrossRefGoogle Scholar
  23. Tu Y, Xie C, Liu C, Li J (2012) Application of Grey correlation analysis method in research evaluation of Qingdong Sag. Nat Gas Geosci 23(2):381–386Google Scholar
  24. Wang X, Hou J, Liu Y, Ji L, Sun J (2017) Studying reservoir heterogeneity by analytic hierarchy process and fuzzy logic, case study of Es1x formation of the Wang guan tun oilfield, China. J Pet Sci Eng 156:858–867CrossRefGoogle Scholar
  25. Wong P, Aminzadeh F, Nikravesh M (2001) Soft computing for reservoir characterization and modeling. Stud Fuzziness Soft Comput 80(5):586–590Google Scholar
  26. Wood DA (2016) Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting. J Nat Gas Sci Eng 28:594–612CrossRefGoogle Scholar
  27. Wu S, Yang Y (2012) Uncertainty and scientific methodology in subsurface reservoir characterization. J Earth Sci Environ 34(2):72–80Google Scholar
  28. Ye C, Wang G, He K, Xu Z (2011) Macro heterogeneity of reservoirs in Sulige gasfield—a case study of the 8th member of the Shihezi formation and the 1th member of the Shanxi formation in the Su53 block. Oil Gas Geol 32(2):236–244Google Scholar
  29. Ye L, Zhong B, Xiong W, Liu H, Hu Z (2012) An integrated evaluation method of Xujiahe low permeability sandstone gas reservoirs in middle Sichuan Basin. Nat Gas Ind 32(11):43–46Google Scholar
  30. Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processe. IEEE Trans Syst Man Cybern 3(1):28–44CrossRefGoogle Scholar
  31. Zeng F, Guo J, Long C (2015) A hybrid model of fuzzy logic and Grey relation analysis to evaluate tight gas formation quality comprehensively. J Grey Syst 27(3):87–98Google Scholar
  32. Zeng D, He Q, Yu Z, Jia W, Zhang S, Liu Q (2017) Risk assessment of sustained casing pressure in gas wells based on the fuzzy comprehensive evaluation method. J Nat Gas Sci Eng 46:756–763CrossRefGoogle Scholar
  33. Zhao P (2004) Quantitative geoscience: methods and its applications. Higher Education Press, Beijing, pp 100–120Google Scholar
  34. Zhu P, Lin C, Ren H, Zhao Z, Zhang H (2015) Micro-fracture characteristics of tight sandstone reservoirs and its evaluation by capillary pressure curves: a case study of Permian sandstones in Ordos Basin, China. J Nat Gas Sci Eng 27:90–97CrossRefGoogle Scholar
  35. Zhu Z, Lin C, Zhang S, Ren L, Zhao J, Chen S, Jia X, Chen L, Zhang J (2017) Application of improved fuzzy-grey comprehensive evaluation method to quantitativereservoir evaluation: a case study of the low-permeability gas reservoirs of the lower part of 8th member of the Shihezi formation in Su X block of Sulige Gasfield. Oil Gas Geol 38(1):197–208Google Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Peng Zhu
    • 1
    Email author
  • Zhaoqun Zhu
    • 2
  • Yuanyuan Zhang
    • 3
  • Lianpu Sun
    • 4
  • Yixin Dong
    • 5
  • Zhiqiang Li
    • 6
  • Ming Chen
    • 7
  1. 1.College of Energy ResourcesChengdu University of TechnologyChengduPeople’s Republic of China
  2. 2.School of Earth Science and EngineeringHebei University of EngineeringHandanPeople’s Republic of China
  3. 3.Exploration and Development Research Institute of PetroChina Changqing Oilfield CompanyXi’anPeople’s Republic of China
  4. 4.Sino Getech Science and Technology Co., Ltd.BeijingPeople’s Republic of China
  5. 5.Institute of Sedimentary GeologyChengdu University of TechnologyChengduPeople’s Republic of China
  6. 6.China Radio Wave Research InstituteXinxiangPeople’s Republic of China
  7. 7.CNOOC (China) Co., Ltd. Zhanjiang BranchZhanjiangPeople’s Republic of China

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