1887

Abstract

Summary

Most problems of production strategy optimization in the oil industry are characterized by a large number of discrete random variables in discontinuous search spaces with non-necessarily monotonic objective functions, usually net present value or oil recovery, with many local maximums within a maximization problem. It demands a large number of simulations to adequately evaluate search space, what becomes more complex when integrating reservoir and production system. This paper evaluates a new iterative discrete Latin Hypercube (IDLHC) sampling based method to maximize the objective function in integrated production strategy optimization.

Inside decision making study, to evaluate the best placement of wells in the reservoir, we have used an optimization process evaluating some objective function. We compared the optimization between the IDLHC and the genetic algorithm method. We used both methodologies to maximize the net present value objective function for the same variable set and search space. We used the benchmark case UNISIM-II-D (carbonate field in Brazil) reservoir model as an application case. And we applied our explicit methodology to integrate reservoir and production system simulators during optimization process.

IDLHC adequately treated posterior frequency distributions of discrete random variables and maximizes nonnecessarily monotonic objective functions within great discontinuous search space and many local optimums set by the well placement problem.

Population based optimization using iterative discrete Latin Hypercube sampling best suited this problem, with consistent convergence to global optimum, few objective function evaluations and the simultaneous multiple numeric reservoir simulations runs.

The IDLHC method showed the advantage of being a simple methodology to maximize the objective function, reducing the search space gradually with each iteration, while addressing posterior frequency distributions of discrete variable levels.

The method successfully maximized the net present value in the well placement step of production strategy optimization, and more faster when compared with a well-established optimization methodology (genetic algorithm).

This easy to use, reliable methodology with lower computational time costs is an interesting option for optimization methods in problems of integrated production strategy design related to the oil industry.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201802213
2018-09-03
2024-03-29
Loading full text...

Full text loading...

References

  1. Beggs, H. D. & Brill
    , 1991. J. P. Two-Phase Flow in Pipes. 6ed.
    [Google Scholar]
  2. BerghF. V. D., EngelbrechtA.P.
    2006. A study of particle swarm optimization particle trajectories. Inf. Sci., 176, pp. 937–971.
    [Google Scholar]
  3. Bittencourt, A. C., Horne, R. N.
    1997. Reservoir Development and Design Optimization. SPE-38895-MS. SPE Annual Technical Conference and Exhibition, Texas. United States.
    [Google Scholar]
  4. Campozana, F. P., Santos, R. L., Madeira, M. G., SousaS. H. G., SpinolaM.
    , 2008. Optimization of Surface Network and Platform Location using a Next Generation Reservoir Simulator Coupled with an Integrated Asset Optimizer - An Application to an Offshore Deep Water Oil Field in Brazil. IPTC-12500-MS. International Petroleum Technology Conference, 3–5 December, Kuala Lumpur, Malaysia. DOI: 10.2523/IPTC‑12500‑MS.
    https://doi.org/10.2523/IPTC-12500-MS [Google Scholar]
  5. Černý, V.
    1985. “Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm”. Journal of Optimization Theory and Applications45: 41–51.
    [Google Scholar]
  6. Correia, M. G.; Hohendorff Filho, J. C. V.; Gaspar, A. T. F. S. and Schiozer, D. J.
    2015. “UNISIM-II-D: Benchmark Case Proposal Based on a Carbonate Reservoir”, SPE LACPEC, November 18–20, Quito, Equator.
    [Google Scholar]
  7. DasS., SuganthanP.N.
    2011. Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput., 15 (1), pp. 4–31.
    [Google Scholar]
  8. Eshelman, L.J., Schaffer, J.D.
    1993. Real-coded genetic algorithm and interval-schemata. In: Foundation of Genetic Algorithms, vol. 2, pp. 187–202.
    [Google Scholar]
  9. Gaspar, A. T. F. S.; Avansi, G. D.; Santos, A. A. S.; Hohendorff Filho, J. C. V.; Schiozer, D. J.
    2015. “UNISIM-I-D: Benchmark Studies for Oil Field Development and Production Strategy Selection”, International Journal of Modelling and Simulation for the Petroleum Industry, v. 9, pp. 47–55.
    [Google Scholar]
  10. Gaspar, A. T. F. S.; Barreto, C. E. A. G.; Schiozer, D. J.
    2016. “Assisted Process for Design Optimization of Oil Exploitation Strategy”, J. Pet. Sci. Eng., doi:10.1016/j.petrol.2016.05.042
    https://doi.org/10.1016/j.petrol.2016.05.042 [Google Scholar]
  11. Goda, T., Sato, K.
    2014. History matching with iterative Latin hypercube samplings and parameterization of reservoir heterogeneity. JPSE. 114, pp. 61–73. [14] Maschio, C.; Schiozer, D. J.2015. An Iterative Procedure for Gradual Uncertainty Reduction in Reservoir Models Attributes Using Observed Data.
    [Google Scholar]
  12. Goldberg, D.E.
    1989. Genetic Algorithms in Search, Optimization & Machine Learning. Addison Wesley, Boston, United States.
    [Google Scholar]
  13. Hohendorff Filho, J. C. V.; Maschio, C.; Schiozer, D. J.
    2016. “Production Strategy Optimization Based on Iterative Discrete Latin Hypercube”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 38, pp. 2473–2480, December.
    [Google Scholar]
  14. Hohendorff Filho, J. C. V., Schiozer, D. J.
    , 2014. Evaluation About Explicit Coupling Between Reservoir Simulators and Production System, Journal of Energy Resources Technology, v. 135, pp. 1–24, October. DOI: 10.1115/1.4028860.
    https://doi.org/10.1115/1.4028860 [Google Scholar]
  15. Hohendorff Filho, J. C. V.; Schiozer, D. J.
    2017. Evaluation of Reservoir and Production System Integration in Production Strategy Selection, SPE RSC, Montgomery, United States, 20–22 February. doi:10.2118/182624‑MS
    https://doi.org/10.2118/182624-MS [Google Scholar]
  16. Kennedy, J., Eberhart, R.
    1995. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948.
    [Google Scholar]
  17. Kirkpatrick, S., Gelatt, C. D.Jr, Vecchi, M. P.
    1983. “Optimization by Simulated Annealing”. Science220 (4598): 671–680.
    [Google Scholar]
  18. StornR., PriceK.
    1997. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim., 11, pp. 341–359.
    [Google Scholar]
  19. Schiozer, D. J., Santos, A. A. S., Drummond, P. S.
    , 2015. Integrated Model Based Decision Analysis in Twelve Steps Applied to Petroleum Fields Development and Management. SPE 174370”. Society of Petroleum Engineers, Europec 2015, Madrid, Spain.
    [Google Scholar]
  20. Standing, M.B.
    , 1947. “A Pressure-Volume-Temperature Correlation for Mixtures of California Oils and Gases,” Drill. & Prod. Prac. 275.
    [Google Scholar]
  21. Victorino, I. R. S.; Hohendorff Filho, J. C. V.; Schiozer, D. J.
    2016. “Sensibility Analysis of Production System Parameters for Integrated Simulation of Reservoir and Production Systems”. Rio Oil & Gas Expo and Conference, October 24–27, Rio de Janeiro, Brazil.
    [Google Scholar]
  22. Yang, C., Nghiem, L., Card, C., Bremeier, M.
    2007. Reservoir Model Uncertainty Quantification through Computer-Assisted History Matching, SPE 109825. SPE Annual Technical Conference and Exhibition, California, United States.
    [Google Scholar]
  23. YARPIZ
    , 2015. Binary and Real-Coded Genetic Algorithms in MATLAB. http://yarpiz.com/. Accessed in January 2018.
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201802213
Loading
/content/papers/10.3997/2214-4609.201802213
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