Skip to main content
SpringerLink
Log in

A study on the construction and optimization of triangular adaptive well pattern

  • ORIGINAL PAPER
  • Published:
Computational Geosciences Aims and scope Submit manuscript

Abstract

An improper well pattern will have considerable adverse effects on the ultimate recovery of oil and gas considering the geological complexities usually associated with reservoirs. Designing an optimal well pattern for a given reservoir is often challenging because of following two categories of reasons: static factors including strong heterogeneities of reservoirs, the existence of outer boundaries, faults and pinch-out belts, variations of sedimentary facie physical properties; dynamic factors including the producers and injectors drilled previously, the multitudes of well patterns and the transformation among them. To overcome the difficulties of designing well patterns under complex conditions, a new method of constructing triangular adaptive well pattern is proposed in this paper. This new triangular adaptive well pattern can adjust the locations of wells spontaneously according to the conditions of reservoirs, achieving optimal effects using fewest wells. Inspired by the similarities between triangular well pattern often encountered in the industry and the triangulation of domains in computational geometry, the well-known Delaunay triangulation is employed to determine the locations of wells. By taking full advantage of the properties of Delaunay triangulation, the construction of triangular adaptive well pattern on the basis of boundaries, faults, and existing wells can be easily obtained and the number of control variables is greatly decreased in the optimization process. Therefore, a gradient-based algorithm coupled with reservoir numerical simulator is used to optimize the well pattern. Compared with conventional regular well patterns, the well pattern proposed here differs in that the scale and orientation of local flooding units are not the same in different parts of the reservoir depending on the geological conditions and the distribution of oil and water in the reservoir. Additionally, the heterogeneity of permeability is taken into account and a uniform displacement of oil in each flooding unit is realized by adjusting the locations of injectors. Detailed results are present ed for two different examples. The results show that the method proposed here can be successfully applied to the construction and optimization of well pattern for large-scale reservoirs.

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

Similar content being viewed by others

References

  1. Güyagüler, B., Horne, R.N., Rogers, L., et al.: Optimization of well placement in a gulf of Mexico waterflooding project. SPE Reserv. Eval. Eng. 5(3), 229–236 (2002)

    Google Scholar 

  2. Norrena, K.P., Deutsch, C.V.: Automatic determination of well placement subject to geostatistical and economic constraints. Society of Petroleum Engineers, 78996-MS. Calgary (2002)

  3. Badru, O., Kabir, C.S.: Well placement optimization in field development. Society of Petroleum Engineers, 84191. Denver (2003)

  4. Özdoǧan, U., Horne, R.N.: Optimization of well placement with a history matching approach. Society of Petroleum Engineers, 90091. Houston (2004)

  5. Bangerth, W., Klie, H., Wheeler, M.F., et al.: On optimization algorithms for the reservoir oil well placement problem. Comput. Geosci. 10, 303–219 (2006)

    Article  Google Scholar 

  6. Zandvliet, M.J., Handels, M., Brouwer, D.R., et al.: Adjoint-based well-placement optimization under production constraints. Society of Petroleum Engineers, 105797. Houston (2007)

  7. Sarma, P., Chen, W.H.: Efficient well placement optimization with gradient-based algorithms and adjoint models. Society of Petroleum Engineers, 112257. Amsterdam (2008)

  8. Morales, A.N., Nasrabadi, H., Zhu, D.: A new modified genetic algorithm for well placement optimization under geological uncertainties. Society of Petroleum Engineers, 143617. Vienna (2011)

  9. Wang, H.G., David, E.C., Durlofsky, L.J., et al.: Optimal well placement under uncertainty using a retrospective optimization framework. SPE J. 17(1), 112–121 (2012)

    Article  Google Scholar 

  10. Nwankwor, E., Nagar, A.K., Reid, D.C.: Hybrid differential evolution and particle swarm optimization for well placement. Comput. Geosci. 17, 249–268 (2013)

    Article  Google Scholar 

  11. Darabi, H., Masihi, M.: Well placement optimization using hybrid optimization technique combined with fuzzy inference system. Petrol. Sci. Tech. 31(5), 481–491 (2013)

    Article  Google Scholar 

  12. Salmachi, A., Sayyafzadeh, M., Haghighi M.: Infill well placement optimization in coal bed methane reservoirs using genetic algorithm.Fuel 111, 248–258 (2013)

    Article  Google Scholar 

  13. Ruifeng, W., Qizhi, H., Xiaoling, Y.: Innovative subsurface and surface optimisation of well placement in stacked heavy oil and light oil pools: A case study. IPTC, 16864. Beijing (2013)

  14. Ma, X., Plaksina, T., Gildin, E.: Integrated horizontal well placement and hydraulic fracture stages design optimization in unconventional gas reservoirs. SPE, 167246. Canada (2013)

  15. Yeten, B., Durlofsky, L.J., Aziz, K.: Optimization of nonconventional well type, location and trajectory. SPE J. 8(3), 200–210 (2003)

    Article  Google Scholar 

  16. Zarei, F., Daliri, A., Alizadeh, N.: The use of neuro-fuzzy proxy in well placement optimization. Soc. Petrol. Eng. 112214 (2008)

  17. Özdoǧan, U., Sahni, A., Yeten, B., et al.: Efficient assessment and optimization of a deepwater asset development using fixed pattern approach. Society of Petroleum Engineers, 95792. Dallas (2005)

  18. Wang, C.H., Li, G.M., Reynolds, A.C.: Optimal well placement for production optimization. Society of Petroleum Engineers, 111154. Lexington (2007)

  19. Zhang, K., Yao, J., Zhang, L., et al.: Optimal control for reservoir production working system using gradient-based methods. The 2nd International Workshop on Intelligent Systems and Applications (ISA2010), p. 1634 (2010)

  20. Emerick, A.A., Silva, E., Messer, B., et al.: Well placement optimization using a genetic algorithm with nonlinear constraints. Society of Petroleum Engineers, 118808. Woodlands (2009)

  21. Onwunalu, J.E., Durlofsky, L.J.: A new well-pattern-optimization procedure for large-scale field development. Society of Petroleum Engineers, 124364-PA. New Orleans (2011)

  22. Frey, W.H.: Selective refinement: A new strategy for automatic node placement in graded triangular meshes. Int. J. Numer. Meth. Eng. 24, 2183–2200 (1987)

    Article  Google Scholar 

  23. Frey, W.H., Field, D.A.: Mesh relaxation: A new technique for improving triangulations. Int. J. Numer. Meth. Eng. 31, 1121–1133 (1991)

    Article  Google Scholar 

  24. Cavendish, J.C., Field, D.A., Frey, W.H.: An approach to automatic three-dimensional finite element mesh generation. Int. J. Numer. Meth. Eng. 21, 329–347 (1985)

    Article  Google Scholar 

  25. Rebay, S.: Efficient unstructured mesh generation by means of Delaunay triangulation and Bowyer–Watson algorithm. Comput. Phys. 105, 125–138 (1993)

    Article  Google Scholar 

  26. Anderson, W.K.: A grid generation and flow solution method for the Euler equations on unstructured grids. Comput. Phys. 110, 23–38 (1994)

    Article  Google Scholar 

  27. de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.: Computational geometry algorithms and applications, pp. 147–151 (2008)

  28. Yang, L., Duan-ping, W., Chuan-Liang, L.: Vectorial well arrangement in anisotropic reservoirs. Pet. Explor. Dev. 33(2), 225–227 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. China University of Petroleum, 66 Changjiang West Road, Qingdao, Shandong, 266555, China

    Kai Zhang, Wenjuan Zhang, Liming Zhang, Jun Yao, Yuxue Chen & Ranran Lu

Authors
  1. Kai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuxue Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ranran Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Yao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, K., Zhang, W., Zhang, L. et al. A study on the construction and optimization of triangular adaptive well pattern. Comput Geosci 18, 139–156 (2014). https://doi.org/10.1007/s10596-013-9388-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-013-9388-5

Keywords

Search

Navigation