Advertisement

Zones Optimization of Low-Permeability Reservoir Based on Multi-attribute of Gray Target Decision

  • Yongchao Xue
  • Xuejiao Zhang
  • Guanyang Ding
Conference paper
Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG)

Abstract

The low-permeability zones optimization can make the best use of low-permeability oil and gas resources. In this paper, ZZ zone, CYG zone in the periphery of Daqing oil field and YHW, BBC3, Z9 zone in Changqing oil field are selected respectively as the target zones, and then the method of gray target decision of multi-attribute with weight under the action of clout distance has been adopted to optimize the five zones and put them in order according to their qualities. For the purpose, main throat radius, mobile fluid percentage, start-up pressure gradient, the percent of clay contents, and crude oil viscosity are chosen as the evaluation indexes. Firstly, gray target decision matrix of multi-attribute aimed at the five target zones is developed, where transform operator of “rewarding superior and punishing inferior” is introduced to standardize the matrix, as a result, positive clout and negative clout of gray target model are obtained successfully. Then, the analytic hierarchy process is employed to obtain feature vector of the evaluation indexes’ judgment matrix, and by normalizing, the relative weights of the five evaluation indexes are calculated in the end. Next, based on the above process, positive and negative clout distance and comprehensive clout distance of target zones are calculated and applied to optimize the best zone. The smaller the comprehensive clout distance is, the better the zone is. The result shows that the best zone is BBC3 in Changqing oil field, while the rest are CYG, YHW, Z9, and ZZ zone. There is a satisfying match between assessment outcome and oil field practical development data, indicating it is effective and feasible to optimize low-permeability zones by means of gray target decision of multi-attribute with weight based on analytic hierarchy process.

Keywords

Low-permeability Action of clout distance Zone optimization Evaluation index Feature vector 

Notes

Acknowledgements

This work has been supported by many people. The authors acknowledge them who contributed to the research and the paper.

References

  1. 1.
    Wang G, Liao R, Li J et al (2007) The development situation and future of low permeability oil reservoirs of SINOPEC. Pet Geol Recovery Effic 14(3):84–89Google Scholar
  2. 2.
    Zou CN, Zhu RK, Wu ST et al (2012) Types, characteristics, genesis and prospects of conventional and unconventional hydrocarbon accumulations: taking tight oil and tight gas in China as an instance. Acta Petrolei Sin 33(2):173–187CrossRefGoogle Scholar
  3. 3.
    Zheng W, Yu L, Sun D (2009) Main affecting factors and special technologies for exploration and exploitation of low-permeability oil and gas resources. Nat Gas Geosci 20(5):651–656Google Scholar
  4. 4.
    Yang Z, Zhang Y, Hao M et al (2006) Comprehensive evaluation of reservoir in low-permeability oilfields. Acta Petrolei Sin 27(2):64–67Google Scholar
  5. 5.
    Wang F, Liu H, Lv G (2014) Steady-state productivity prediction model for long-length fractured vertical well in low permeability oil reservoirs. Pet Geol Recovery Effic 21(1):84–86Google Scholar
  6. 6.
    Ynag Z, Jiang H, Zhu G et al (2008) Research on reservoir evaluation index for low-permeability water-bearing gas reservoir. Acta Petrolei Sin 29(2):252–255CrossRefGoogle Scholar
  7. 7.
    Lin S, Zou C, Yuan X et al (2011) Status quo of tight oil exploitation in the United States and its implication. Lithol Reservoirs 23(4):25–30Google Scholar
  8. 8.
    Zhao Y, Cheng Y, Liu Y et al (2013) Study on influence of start-up pressure gradient to micro-seepage in low permeability reservoirs and development trends. Pet Geol Recovery Effic 20(1):67–69Google Scholar
  9. 9.
    Sun Y (2008) Research on comprehensive evaluation method for low permeability reservoir. Institute of Porous Flow and Fluid Mechanics of CASGoogle Scholar
  10. 10.
    Zeng BQ, Cheng LS, Li CL (2013) Application and optimization of a fractured horizontal well in an ultralow permeability reservoir. Pet Sci Technol 31(9):977–990CrossRefGoogle Scholar
  11. 11.
    Zhang J, Huang G, Li J et al (2015) Seismic favorable reservoir prediction based on analytic hierarchy process. Spec Oil Gas Reservoirs 5:23–27Google Scholar
  12. 12.
    Wen H, Sun N, Xu K et al (2007) Optimization of suction parameter of pumping well in low permeability reservoir from grey analytic hierarchy process. Fault Block Oil Gas Field 14(5):74–76Google Scholar
  13. 13.
    Deng X, Li J, Zeng H et al (2012) Research on computation methods of AHP weight vector and its applications. Math Pract Theory 42(7):93–100Google Scholar
  14. 14.
    He Y (2011) A quantitative reservoir evaluation method based on fuzzy comprehensive appraisal and analytical hierarchy process-case of Xujiahe formation, Baojie area. Pet Geol Recovery Effic 18(1):23–25Google Scholar
  15. 15.
    Dang Y, Liu G, Wang J et al (2004) Multi-objective grey target decision model with weight. Control Decis 3:29–30Google Scholar
  16. 16.
    Liang B, Dai Y, Chen T et al (2013) Application of a grey target decision model based on interval numbers of geological parameters to optimize shale gas exploration and development target zones. Nat Gas Ind 33(12):54–59Google Scholar
  17. 17.
    Song J, Dang Y, Wang Z et al (2010) New decision model of grey target with both the positive clout and the negative clout. Syst Eng Theory Pract 30(10):1822–1827Google Scholar
  18. 18.
    Luo D (2013) Multi-objective grey target decision model based on positive and negative clouts. Control Decis 28(2):241–246Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.China University of PetroleumBeijingChina

Personalised recommendations