1887

Abstract

Summary

Since optimal well locations and operational conditions are dependent on each other, these variables have been recently optimized together. Particle swarm optimization (PSO) algorithm is a global optimization method, which is computationally less attractive. Ensemble based optimization (EnOpt) method is a gradient based optimization method, which converges fast but is susceptible to get trapped into a local optima. In this paper, we propose a new hybrid algorithm PSO-EnOpt for the joint optimization problem. By combining PSO and EnOpt algorithms, PSO-EnOpt can take the advantages of the both algorithms. In the PSO-EnOpt algorithm, PSO locates the optimizing vector near global optima. Then, EnOpt finds a global solution with fast converge rates. Therefore, PSO-EnOpt can have faster converge rates compare to the PSO algorithm. Also, it can provide more stable results than EnOpt algorithm due to the global search ability of PSO. We apply the proposed algorithm to determine an optimal injection well location, injection rates, and producing bottomhole pressures. PSO-EnOpt shows superior performance compare to other preexisting algorithms. The proposed algorithm can be applicable to real field development problems and help decision makers to make rapid and optimal decisions.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201600760
2016-05-30
2024-03-29
Loading full text...

Full text loading...

References

  1. Chen, Y., Oliver, D.S., and Zhang. D.
    [2009] Efficient Ensemble-Based Closed-Loop Production Optimization. SPE Journal, 14(4) 634–645. DOI: 10.2118/112873‑PA.
    https://doi.org/10.2118/112873-PA [Google Scholar]
  2. JinJ., KangM., and ChoeJ.
    [2015] Optimal well positioning under geological uncertainty by equalizing the arrival time. Energy Exploration & Exploitation, 33(5), 677–688.
    [Google Scholar]
  3. Kennedy, J., and R.Eberhart.
    [1995] A new optimizer using particle swarm theory, In: Proceedings of the Sixth International Symposium on Micro machines and Human Science, 39–43.
    [Google Scholar]
  4. Lorentzen, R. J., Berg, A., Nævdal, G., and Vefring, E. H.
    [2006] A New Approach For Dynamic Optimization Of Water Flooding Problems. SPE Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands, April 11–13, 2006. DOI: 10.2118/99690‑MS.
    https://doi.org/10.2118/99690-MS [Google Scholar]
  5. Zhao, H., Chen, C., Do, S., Oliveira, D., Li, G., and Reynolds, A.
    [2013] Maximization of a Dynamic Quadra tic Interpolati on Model for Producti on Optimization. SPE Journal, 18(6) 1012–1025. DOI: 10.2118/141317‑PA.
    https://doi.org/10.2118/141317-PA [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201600760
Loading
/content/papers/10.3997/2214-4609.201600760
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error