Investigating Proxy Models for a Production System in Integrated Simulations with Oil Reservoir

Document Type : Research Paper

Authors

Center for Energy and Petroleum Studies, University of Campinas, Campinas, Brazil

Abstract

This work evaluates the Proxy model application representing the production system for integrated simulation with a reservoir to reduce computational time while preserving the representativeness of financial return and hydrocarbon production behavior relative to a reference model. It includes specific Proxy models for production systems in integrated simulations that include their geometrical parameters, focusing on field production strategy optimization. The production system’s Proxy models are developed through response surface methodology (RSM) and artificial neural network (ANN), which are generated and validated from a medium fidelity model (MFM). The validation is performed by cross-checking simulations. The developed RSM-based Proxy model obtained the highest representativeness by combining discrete variables (pipe segment diameters and the gas flow rate for artificial lift) with split continuous variables (lengths of the production column and flowline, liquid rate, and water cut) using several response surfaces. The developed ANN-Based Proxy model enhanced representativeness by combining all variables and increasing the number of MFM samples for ANN training. The RSM-Based Proxy model was selected due to its lower residual value than the ANN-Based Proxy model. The results from the production strategy of the simulated Proxy model in the MFM showed a difference of 4% in net present value compared to the simulation of the reference model, with both strategies obtained inside a production strategy optimization process. The reduction of computational time was close to 30% with the selected Proxy model, which it presents an advantage of using the proposed approach in optimization applications. The developed methodology provides an alternative to replace more robust production system models in integrated simulations with several advantages, such as: reduction of computational times, applications in more complex problems, and better-exploring uncertainties, and thereby,  faster decision-making is obtained.

Keywords

References

  1. Kosmala, A., Aanonsen, S. I., Gajraj, A., Biran, V., Brusdal, K., Stokkenes, A. & Torrens, R. (2003). Coupling of a surface network with reservoir simulation, SPE 84220. Richardson: Society of Petroleum Engineers, doi: 10.2118/84220-MS.##
  2. Rotondi, M., Cominelli, A., Di Giorgio, C., Rossi, R., Vignati, E. & Caraati, B. (2008). The benefits of integrated asset modeling: lesson learned from field cases, SPE 113831, SPE Europec/EAGE Annual Conference and Exhibition held in Rome, Italy, 9-12, doi: 10.2118/113831-MS. ##
  3. Hohendorff Filho, J. C. V. & Schiozer, D. J. (2018a). Effect of Reservoir and Production System Integration on Field Production Strategy Selection, Oil & Gas Science and Technology, 73(1-15), doi: 10.2516/ogst/2018042. ##
  4. Victorino, I. R. S., Hohendorff Filho, J. C. V., Castro, M. S., Mello, S. F. & Schiozer, D. J. (2018) Influence of Well and Gathering Systems Parameters on Integrated Petroleum Reservoir and Production System Simulations, Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 40, 9( 1-21), doi: 10.1007/s40430-018-1341-z. ##
  5. Lopes, M., von Hohendorff Filho, J. & Schiozer, D. J. (2021). The effect of dynamic data adjustments in production system simulation models on oil production forecasting applied to reservoir simulation models, Journal of Petroleum Science and Technology, 11(1), 17-28. doi: 10.22078/jpst.2021.4339.1701. ##
  6. Gaspar, A. T., Barreto, C. E., Muñoz Mazo, E. O., and Schiozer, D. J. (2014). Application of assisted optimization to aid oil exploitation strategy selection for offshore fields, SPE Latin America and Caribbean Petroleum Engineering Conference, doi: 10.2118/169464-MS. ##
  7. Silva, L. M., Avansi, G. D. and Schiozer, D. J. (2020). Development of proxy models for petroleum reservoir simulation: a systematic literature review and state-of-the-art, International Journal of Advanced Engineering Research and Science, 7(10), 36–62. doi:10.22161/ijaers.710.5. ##
  8. Maschio, C., Avansi, G. D., Silva, F. B. M. and Schiozer, D. J. (2022). Data assimilation for uncertainty reduction using different fidelity numerical models, Journal of Petroleum Science and Engineering, 209, 2022, 109851, ISSN 0920-4105, doi: 10.1016/j.petrol.2021.109851. ##
  9. Shields, A., Tihonova, S., Stott, R., Saputelli, L. A., Haris, Z. & Verde, A. (2015). Integrated Production Modelling for CSG Production Forecasting, Society of Petroleum Engineers, doi:10.2118/176881-MS. ##
  10. Mohaghegh, S. D. & Abdulla, F. A. S. (2014). Production management decision analysis using ai-based proxy modeling of reservoir simulations – a look-back case study, Society of Petroleum Engineers, 27, doi: 10.2118/170664-MS. ##
  11. Peng, C. H. & Gupta, R. (2003). Experimental design in deterministic modelling: assessing significant uncertainties, SPE 80537. SPE Asia Pacific Oil and Gas Conference, Jakarta, Indonesia, September 9-11. doi: 10.2118/80537-MS. ##
  12. Risso, F. V. A., Risso, V. F. & Schiozer, D. J. (2007). Risk assessment of oil fields using proxy models: a case study, In: 8th Canadian International Petroleum Conference, Calgary, Alberta, Canada, 12-14. doi: 10.2118/2007-138. ##
  13. Madeira, M. G. (2005). Comparison of techniques for risk analysis applied to petroleum field development, Campinas: Petroleum Engineering Department, Mechanical Engineering Faculty, State University of Campinas, Dissertation, 132. ##
  14. Schiozer, D. J., Ligero, E. L., Maschio, C., Risso, F. V. A. (2008). Risk assessment of petroleum fields – use of numerical simulation and proxy models, Petroleum Science and Technology, 26, 10-11(1247-1266), doi: 10.1080/10916460701833913. ##
  15. Risso, F. V. A., Risso, V. F. & Schiozer, D. J. (2006). Application of statistical design and proxy models in risk analysis of petroleum fields, In: Rio Oil & Gas. Rio de Janeiro, Brazil, 11-14. ##
  16. Denney, D. (2010). Pros and cons of applying a proxy model as a substitute for full reservoir simulations, Society of Petroleum Engineers, doi:10.2118/0710-0041-JPT. ##
  17. Hohendorff Filho, J. C. V. & Schiozer, D. J. (2019). Methodology to accelerate explicit integration between reservoir and production system simulators, 11/2019, XL CILAMCE, 1, 1-3, Natal, RN, Brazil. ##
  18. Venkataraman, R. (2000). Application of the method of experimental design to quantify uncertainty in production profiles, SPE 59422. SPE Asia Pacific Conference on Integrated Modelling for Asset Management, Yokohama, Japan, 25-26, doi: 10.2118/59422-MS. ##
  19. Oliveira, F. S., Gomes, D. M., Cavalcante, J. R. & Leitão, H.C. (2015). Optimizing steam injection scheduling using analytical models in a probabilistic approach, In: SPE Canada Heavy Oil Technical Conference, Calgary, Alberta, Canada. doi: doi.org/10.2118/174427-MS. ##
  20. Camponogara, E. & Nakashima, P. H. R. (2006) Optimal allocation of lift-gas rates under multiple facility constraints: a mixed integer linear programming approach, Journal of Energy Resources Technology, 128(4), 280-289, doi: 10.1115/1.2358143. ##
  21. Silva, T. L. & Camponogara, E. (2014). A computational analysis of multidimensional piecewise-linear models with applications to oil production optimization, European Journal of Operational Research, 232, 3(630-642), doi:10.1016/j.ejor.2013.07.040. ##
  22. Luguesi, C., Camponogara, E., Seman, L. O., González, J. T. & Leithardt, V. R. Q. (2023). Derivative-free optimization with proxy models for oil production platforms sharing a subsea gas network, IEEE Access, 11(8950-8967), doi: 10.1109/ACCESS.2023.3239421. ##
  23. Kohler, M., Vellasco, M., Silva, E. & Figueiredo, K. (2020). simproxy decision support system: a neural network proxy applied to reservoir and surface integrated optimization, IEEE Systems Journal, 14, 4(5111-5120), doi: 10.1109/JSYST.2020.2968239. ##
  24. Hohendorff Filho, J. C. V. & Schiozer, D. J. (2018b). Integrated production strategy optimization based on iterative discrete latin hypercube, ECMOR XVI, 3-6, Barcelona, Spain, doi: 10.3997/2214-4609.201802213. ##
  25. Avansi, G. D., Schiozer, D. J., Suslick, S. B., Risso, F. V. A. (2009). Assisted procedures for definition of production strategy and economic evaluation using proxy models, SPE 122298, Annual Conference and Exhibition held in Amsterdam, The Netherlands, 8–11 doi: 10.2118/122298-MS. ##
  26. Heris, M. K. (2015). Group method of data handling (GMDH) in MATLAB (URL: yarpiz.com/263/ypml113-gmdh), Yarpiz. ##
  27. Alexey, I. (1971). Polynomial theory of complex systems (PDF), IEEE Transactions on Systems, Man and Cybernetics, SMC-1 (4), 364–378, doi:10.1109/TSMC.1971.4308320. ##
  28. Negash, B. M., Tufa, L. D., Ramasamy, M., & Awang, M. B. (2017). System identification based proxy model of a reservoir under water injection. Modelling and Simulation in Engineering, 1–10, doi: 10.1155/2017/7645470. ##
  29. Victorino, I. R. S., Hohendorff Filho, J. C. V., Castro, M. S. & Schiozer, D. J. (2019). Analysis of the production of a pre-salt based carbonate reservoir through integrated simulation of reservoir and production system, International Journal of Petroleum Engineering, 3, doi: 10.1504/IJPE.2019.105645. ##
  30. Correia, M. G., Hohendorff Filho, J. C. V., Gaspar, A. T. F. S. & Schiozer,. D. J. (2015). UNISIM-II-D: benchmark case proposal based on a carbonate reservoir, SPE LACPEC, 18-20 November, Quito, Equator, doi: 10.2118/177140-MS. ##
  31. Santos, A. A. S., Gaspar, A. T., Hohendorff Filho, J. C. V., Correia, M. G., Santos, S. M. G. & Schiozer, D. J. (2018). Case study for field development and management - selection of production strategy based on UNISIM-II, Available in: https://www.unisim.cepetro.unicamp.br/benchmarks/files/UNISIM-II-D.pdf. ##
  32. Beggs HD, Brill JP (1991) Two-Phase Flow in Pipes. 6ed. ##
  33. Standing, M. B. (1947). A pressure-volume-temperature correlation for mixtures of california oils and gases, Drill, & Prod. Prac. 275. ##
  34. 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, 38(2473–2480),doi: 10.1007/s40430-016-0511-0. ##

Files

Share

How to cite

Statistics