Skip to main content
SpringerLink
Log in

Optimizing Injection Process of Water-Alternate-Gas Using Different Produced Gas Densities in Enriched-Gas Flooding

  • Published:
Chemistry and Technology of Fuels and Oils Aims and scope

An efficient optimization and design method has been proposed and developed for Enriched-gas Water-Alternating-Gas (EWAG) injection process. The proposed technique is able to quantitatively determine the sizes of the enriched-gas slug and water slug for each cycle of the water-alternating-gas (WAG) injection process, as well as the total number of injection cycles. Applying this method provides the opportunity to implement the WAG scenario more efficient and economical. The numerical simulation showed that in comparison with other conventional WAG scenarios with traditional optimization approach, the EWAG has the obvious advantages of evaluation of indices, such as the oil recovery factor and cumulative net cash flow.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Similar content being viewed by others

Abbreviations

CGI:

Continuous gas injection

LPG:

Liquefied petroleum gas

MME:

Minimum miscible enrichment

OOIP:

Original oil in place

PV:

Pore volume

ROIP:

Remaining oil in place

TRF:

Tertiary Recovery Factor

WAG:

Water-alternating-gas

WF:

Water flooding

References

  1. A. Arya, T. A. Hewett, R. G. Larson, and L. W. Lake, “Dispersion and reservoir heterogeneity,” SPE Resent. Eval. Eng., 3, No. 1, 139-148 (1988).

    Article  CAS  Google Scholar 

  2. A. R. Awan, R. Teigland, and J. Kleppe, “A survey of North Sea enhanced-oil-recovery projects initiated during the years 1975 to 2005,” SPE Reserv. Eval. Eng., 11, No. 3, 497-512 (2008).

    Article  CAS  Google Scholar 

  3. L. Bermudez, R. T. Johns, and H. C. Parakh, “Parametric investigation of WAG floods above the MME,” SPE J., 12, No. 2, 224-234 (2007).

    Article  CAS  Google Scholar 

  4. J. R. Christensen, E. H. Stenby, and A. Skauge, “Review of WAG field experience,” SPE Reserv. Eval. Eng., 4, No. 2, 97-106 (2001).

    Article  CAS  Google Scholar 

  5. S. Chen, H. Li, D. Yang, and P. Tontiwachwuthikul, “Optimal parametric design for water-alternating-gas (WAG) process in a CO2miscible flooding reservoir,” J. Can. Pet. Technol., 49, No. 10, 75-82 (2010).

    Article  CAS  Google Scholar 

  6. T. E. H. Esmaiel, S. Fallah, and C. P. J. W. van Kruijsdijk, “Determination of WAG ratios and slug sizes under uncertainty in a smart wells environment,” SPE 93569, 14th SPE Middle East Oil and Gas Show and Conference, Bahrain International Exhibition Centre, Bahrain (2005).

  7. R. E. Hadlow, “Update of industry experience with CO2 injection,” In SPE Annual Technical Conference and Exhibition, Society of Petroleum Engineers (1992).

  8. L. W. Holm and V. A. Josendal, “Discussion of determination and prediction of CO2 minimum miscibility pressures,” J. Pet. Technol., 32, No. 5 (1980).

  9. E. T. S. Huang and L.W. Holm, “Effect of WAG injection and wettability on oil recovery during carbon dioxide flooding,” SPE 15491, 1986 Annual Technical Conference and Exhibition, New Orleans, LA, U.S.A. (1986).

  10. R. T. Johns, F. J. Fayers, and F. M. Orr, “Effect of gas enrichment and dispersion on nearly miscible displacements in condensing/vaporizing drives,” SPE Adv. Technol. Ser., 2, No. 2, 26-34 (1993).

    Article  Google Scholar 

  11. M. M. Kulkarni, “Immiscible and miscible gas-oil displacements in porous media,” Master’s thesis, The Craft and Hawkins Department of Petroleum Engineering, University of Pune, India (2003).

  12. L. L. Lo, D. S. McGregor, and P. Wang, “WAG pilot design and observation well data analysis for HassiBerkine South Field,” SPE 84076, SPE Annual Technical Conference and Exhibition, Denver, Colorado, U.S.A. (2003).

  13. T. D. Ma, J. A. Rugen, R. F. Stoisits, and G. K. Youngren, “Simultaneous water and gas injection pilot at the Kuparuk River Field, reservoir impact,” SPE Annual Technical Conference and Exhibition, 22-25 October, Dallas, Texas, U.S.A. (1995).

  14. J. Mahadevan, L. W. Lake, and R. T. Johns, “Estimation of true dispersivity in field scale permeable media,” SPE/DOE Improved Oil Recovery Symposium, 13-17 April, Tulsa, Oklahoma, U.S.A. (2002).

  15. D. T. Raj, R. Slamet, K. Charles, A. K. Farihan, A. B. Mohamad, R. T. O. Tengku, and B. Nazrin, “Maximizing the oil recovery through immiscible water alternating gas (IWAG) in mature offshore field,” SPE:133345- MS, SPE Asia Pacific Oil and Gas Conference and Exhibition, Brisbane, Queensland, Australia (2010).

  16. J. D. Rogers and R. B. Grigg, “A literature analysis of the WAG injectivity abnormalities in the CO2 process,” SPE Reserv. Eval. Eng., 4, No. 5, 375-386 (2001).

    Article  CAS  Google Scholar 

  17. G. M. Rouzbeh and W. L. Larry, “Simultaneous water-gas-injection performance under loss of miscibility,” SPE: 129966-MS, SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, U.S.A. (2010).

  18. A. Skauge and E. I. Dale, “Progress in immiscible WAG modelling,” SPE/EAGE Reservoir Characterization and Simulation Conference, 28-31 October, Abu Dhabi, UAE (2007).

  19. M. Sohrabi, D. H. Tehrani, A. Danesh, and G. D. Henderson, “Visualization of oil recovery by water-alternating-gas injection using high-pressure micromodels,” SPE J., 9, No. 3, 290-301 (2004).

    Article  CAS  Google Scholar 

  20. R. Solano, R. T. Johns, and L. W. Lake, “Impact of reservoir mixing on recovery in enriched-gas drives above the minimum miscibility enrichment,” SPE Reserv. Eval. Eng., 4, No. 5, 358-365 (2001).

    Article  CAS  Google Scholar 

  21. F. L. Stalkup, “Effect of gas enrichment and numerical dispersion on enriched-gas-drive predictions,” SPE Reserv. Eval. Eng., 5, No. 4, 647-655 (1990).

    Article  CAS  Google Scholar 

  22. L. M. Surguchev, R. Korbol, and O. S. Krakstad, “Optimum water alternate gas injection schemes for stratified reservoirs,” SPE Annual Technical Conference and Exhibition, 4-7 October, Washington, D.C., U.S.A. (1992).

  23. R. D. Tewari, S. Riyadi, C. Kittrell, F. A. Kadir, M. Abu Bakar, T. Othman and N. Banu, “Maximizing the oil recovery through immiscible water alternating gas (IWAG) in mature offshore field,” SPE Asia Pacific Oil and Gas Conference and Exhibition, 18-20 October, Brisbane, Qeensland, Australia (2010).

  24. M. K. Zahoor, M. N. Derahman, and M. H. Yunan, “Wag process design - an updated review,” Brat. J. Pet. Gas, 5, No. 2 (2011).

  25. Y. Wang, “Novel existence and uniqueness criteria for periodic solutions of a Duffing type p-Laplacian equation,” Appl. Math. Lett., 23, No. 4, 436-439 (2010).

    Article  Google Scholar 

  26. L. Zhang, Y. Wang, H. X. Li, and L. Zhang, “Periodic solutions and global attraction for N-dimension discrete-time neural networks with time-varying delays,” Bull. Belg. Math. Soc.-Simon Steven, 18, No. 3, 483-491 (2011).

    Article  Google Scholar 

  27. J. Tian, S. Zhong, and Y. Wang, “Improved exponential stability criteria for neural networks with time-varying delays,” Neurocomputing, 97, 164-173 (2012).

    Article  Google Scholar 

  28. X. Dai, Z. Zhang, Y. Wang, J. Li, and L. Chen, “Multi-parameter magnetoelectric response modeling of magnetostrictive/piezoelectric laminate composites considering shear strain,” J. Appl. Phys., 115, 014104 (2014).

    Article  Google Scholar 

  29. Y. Wang and X. Y. Yi, “Some results for periodic solutions of a kind of Liénard equation,” J. Funct. Space, 519747 (2015).

  30. Y. Wang and X. Y. Yi, “Transient pressure behavior of a fractured vertical well with a finite-conductivity fracture in triple media carbonate reservoir,” J. Porous Media, 20, No. 8, 707-722 (2017).

    Article  Google Scholar 

  31. Y. Wang, Z. W. Tao, L. Chen, and X. Ma, “The nonlinear oil—water two-phase flow behavior for a horizontal well in triple media carbonate reservoir,” Acta Geophys., 65, No. 5, 977-989 (2017).

    Article  Google Scholar 

  32. Y. Wang, C. Zhang, T. Chen, and X. Ma, “Modeling the nonlinear flow for a multiple-fractured horizontal well with multiple finite-conductivity fractures in triple media carbonate reservoir,” J. Porous Media, 21, No. 12, 1283-1305 (2018).

    Article  Google Scholar 

  33. Y. Wang, C. Zhang, R. Huang, H. T. Cao, and X. Ma, “Modeling oil-water two-phase flow behavior of a fractured vertical well with a finite-conductivity fracture in triple media carbonate reservoir,” Adv. Appl. Math. Mech., 10, No. 3, 581-610 (2018).

    Article  CAS  Google Scholar 

  34. Y. Wang, M. J. Li, X. Ma, W. B. Gao, G. Q. Xue, and J. Zhou, “Modeling the nonlinear oil-water two-phase flow behavior for a multiple-fractured horizontal well in triple media carbonate reservoir,” Adv. Appl. Math. Mech., 10, No. 4, 998-1024 (2018).

    Article  Google Scholar 

  35. Y. Wang and X. Y. Yi, “Flow modeling of well test analysis for a multiple-fractured horizontal well in triple media carbonate reservoir,” Int. J. Nonlin. Sci. Numer Simla., 19, No. 5, 439-457 (2018).

    Article  CAS  Google Scholar 

  36. W. Q. Wu, X. Ma, B. Zeng, Y. Wang, and W. Cai, “Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption,” Energy, 165, 223-234 (2018).

  37. W. Q. Wu, X. Ma, Y. Y. Zhang, W. P. Li, and Y. Wang, “A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries,” Sci. Total Environ., 707, 135447 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. W. Q. Wu, X. Ma, B. Zeng, Y. Wang, and W. Cai, “Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model,” Renew. Energy, 140, 70-87 (2019).

    Article  Google Scholar 

  39. X. Ma, W. Q. Wu, B. Zeng, Y. Wang, and X. X. Wu, “The conformable fractional grey system model,” ISA Trans., 96, 255-271 (2020).

    Article  PubMed  Google Scholar 

  40. W. Q. Wu, X. Ma, Y. Wang, Y. Y. Zhang, and B. Zeng, “Research on a novel fractional GM (alpha, n) model and its applications,” Grey Sys -- Them, Appl., 9, No. 2, 356-373 (2019).

  41. X. Ma, M. Xie, W. Q. Wu, B. Zeng, Y. Wang, and X. X. Wu, “The novel fractional discrete multivariate grey system model and its applications,” Appl. Math. Model., 70, 402-424 (2019).

    Article  Google Scholar 

  42. Y. Wang, D. H. Tian, G. F. Li, C. Zhang, and T. Chen, “Dynamic analysis of a fractured vertical well in triple media carbonate reservoir,” Chem. Technol. Fuels Oils, 55, No. 1, 56-65 (2019).

    Article  CAS  Google Scholar 

  43. W. Q. Wu, X. Ma, Y. Y. Zhang, Y. Wang, and X. X. Wu, “Analysis of novel FAGM(1,1,t) model to forecast health expenditure of China,” Grey Syst. — Theory Appli., 9, No. 2, 232-250 (2019).

  44. Y. Wang, Z. W. Tao, D. H. Tian, X. Ma, M. J. Li and Z. H. Feng, “Some novel results of T-periodic solutions for Rayleigh type equation with double deviating arguments,” Univ. Politeh. Buchar Sci. Bull. - Ser A - Appl. Math. Phys., 82 (2020).

  45. X. Ma, W. Q. Wu, Y. Wang, B. Zeng, and W. Cai, “Predicting primary energy consumption using NDGM(l,l,k,c) model with Simpson formula,” Sci. Iran., 27 (2020).

  46. Y. Wang, Z. W. Tao, K. Yang, X. Ma, D. H. Tian, and Z. H. Feng, “Dynamic analysis of oil-water two-phase flow for a multiple-fractured horizontal well with multiple finite-conductivity fractures in triple media carbonate reservoir,” ZAMM-Z. Angew. Math. Mech., 100, e201900046 (2020).

    Google Scholar 

  47. Y. L. Zhao, B. C. Shan, L. H. Zhang and Q. G. Liu, “Seepage flow behaviors of multi-stage fractured horizontal wells in arbitrary shaped shale gas reservoirs,” J. Geophys. Eng., 13, No. 5, 1-10 (2016).

    Article  Google Scholar 

  48. Y. L. Zhao, L. H. Zhang, Y. Xiong, Y. H. Zhou, Q. G. Liu, and D. Chen, “Pressure response and production performance for multi-fractured horizontal wells with complex seepage mechanism in box-shaped shale gas reservoir,” J. Nat. Gas Sci. Eng., 32, 66-80 (2016).

    Article  Google Scholar 

  49. Y. L. Zhao, L. H. Zhang, G. Q. Feng, B. N. Zhang, and B. Kang, “Performance analysis of fractured wells with stimulated reservoir volume in coal seam reservoirs,” Oil Gas Sci. Technol., 71, No. 1, 1-8 (2016).

    Article  Google Scholar 

  50. J. J. Ren and P. Guo, “A general analytical method for transient flow rate with the stress-sensitive effect.” J. Hydrol., 565, 262-275 (2018).

    Article  Google Scholar 

  51. J. J. Ren and P. Guo, “Analytical method for transient flow rate with the effect of the quadratic gradient term,” J. Pet. Sci. Eng., 162, 774-784 (2018).

    Article  CAS  Google Scholar 

  52. J. J. Ren and P. Guo, “Nonlinear flow model of multiple fractured horizontal wells with stimulated reservoir volume including the quadratic gradient term,” J. Hydrol., 554, 155-172 (2017).

    Article  Google Scholar 

  53. J. J. Ren and P. Guo, “Performance of vertical fractured wells with multiple finite-conductivity fractures,” J. Geophys. Eng., 12, No. 6, 978-987 (2015).

    Article  Google Scholar 

  54. J. J. Ren and P. Guo, “Anomalous diffusion performance of multiple fractured horizontal wells in shale gas reservoirs,” J. Nat Gas Sci. Eng., 26, 642-651 (2015).

    Article  Google Scholar 

  55. J. J. Ren, P. Guo and S. Peng, “Performance of multi-stage fractured horizontal wells with stimulated reservoir volume in tight gas reservoirs considering anomalous diffusion,” Environ. Earth Sci., 77, No. 22 (2018).

  56. D. H. Tian, B. W. Yang, J. H. Chen, and Y. Zhao, “A multi-experts and multi-criteria risk assessment model for safety risks in oil and gas industry integrating risk attitudes,” Knowledge-Based Syst., 156, 62-73 (2018).

    Article  Google Scholar 

  57. D. H. Tian, C. L. Zhao, B. Wang, and M. Zhou, “A MEMCIF-IN method for safety risk assessment in oil and gas industry based on interval numbers and risk attitudes,” Eng. Appl. Artif. Intell., 85, 269-283 (2019).

    Article  Google Scholar 

  58. D. H. Tian, Y. Wang, and T. Yu, “Fuzzy risk assessment based on interval numbers and assessment distributions,” Int. J. Fuzzy Syst., 22 (2020).

  59. J. Y. Xiao, S. P. Wen, X. J. Yang, and S. M. Zhong, “New approach to global Mittag-Leffler synchronization problem of fractional-order quaternion-valued BAM neural networks based on a new inequality,” Neural Netw., 122, 320-337 (2020).

    Article  PubMed  Google Scholar 

  60. J. Y. Xiao and S. M. Zhong, “Synchronization and stability of delayed fractional-order memristive quaternion-valued neural networks with parameter uncertainties,” Neurocomputing, 363,321-338 (2019).

    Article  Google Scholar 

  61. Z. H. Feng, F. Y. Li and J. X. Liu, “Notes on a boundary value problem with a periodic nonlinearity,” Optik, 156, 439-446 (2018).

    Article  Google Scholar 

  62. Z. H. Feng, F. Y. Li, Y. Lv, and S. Q. Zhang, “A note on Cauchy-Lipschitz-Picard theorem,” J. Inequal. Appl., 271, 1-6 (2016).

    Google Scholar 

  63. Z. H. Feng, R Wu, and H. X. Li, “Multiple solutions for a modified Kirchhoff-type equation in R^N” Math. Methods. Appl. Sci., 38, No. 4,708-725 (2015).

  64. Z. H. Feng, X. X. Wu, and L. Yang, “Stability of a mathematical model with piecewise constant arguments for tumor-immune interaction under drug therapy,” Int. J. Bifinv. Chaos, 29, No. 1,1950009 (2019).

Download references

Acknowledgments

This work was supported by the Program of Science and Technology of Sichuan Province of China (No. 20YYJC01 45).

Author information

Authors and Affiliations

  1. School of Sciences, Southwest Petroleum University, Chengdu, China

    Yong Wang & Donghong Tian

  2. Research Institute of Exploration and Development, Tarim Oilfield Company, PetroChina, Korla, China

    Zhengwu Tao

  3. School of Science, Southwest University of Science and Technology, Mianyang, China

    Xin Ma

  4. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, China

    Yong Wang

  5. School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou, China

    Zonghong Feng

Authors
  1. Yong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhengwu Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donghong Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zonghong Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Wang.

Additional information

Translated from Khimiya i Tekhnologiya Topliv i Masel, No. 2, pp. 93 – 100, March – April, 2020.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Tao, Z., Tian, D. et al. Optimizing Injection Process of Water-Alternate-Gas Using Different Produced Gas Densities in Enriched-Gas Flooding. Chem Technol Fuels Oils 56, 271–284 (2020). https://doi.org/10.1007/s10553-020-01137-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10553-020-01137-3

Keywords

Search

Navigation