43
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Comparative study on the optimal control of smart well in oil reservoir waterflooding with uncertainty

ORCID Icon, ORCID Icon & ORCID Icon
Pages 78-86 | Received 01 Jul 2023, Accepted 31 Jan 2024, Published online: 27 Feb 2024

References

  • Adeniyi, O. D., Nwalor, J. U., & Ako, C. T. (2008). A review on waterflooding problems in Nigeria’s crude oil production. Journal of Dispersion Science and Technology, 29(3), 362–365. https://doi.org/10.1080/01932690701716101
  • Ambia, F. (2012). A robust optimization tool based on stochastic optimization methods for waterflooding project. SPE Journal, (October), SPE-160907–STU. https://doi.org/10.2118/160907-STU
  • Augusto, M. P. S., Ghasemi, M., Sorek, N., Eduardo, G., & Denis, J. S. (2015). Hybrid optimization for closed-loop reservoir management. SPE Journal, 1, SPE-173278–MS. https://org/SPE-173278-MS
  • Balaji, K., Suhag, A., Ranjith, R., Yegin, C., Saracoglu, O., Hendroyono, A., & Dhannoon, D. (2017). Optimization of recovery in waterfloods with bang-bang control in reservoirs with subsidence and uplift. SPE Journal, SPE-185727–MS. https://doi.org/10.2118/185727-MS
  • Ben-Tal, A., & Nemirovski, A. (2002). Robust optimization-methodology and appplication. Mathematical Programming, 92(3), 453–480. https://doi.org/10.1007/s101070100286
  • Beyer, H., & Sendhoff, B. (2007). Robust optimization – A comprehensive survey. Computer Methods in Applied Mechanics and Engineering, 196(33–34), 3190–3218. https://doi.org/10.1016/j.cma.2007.03.003
  • Brouwer, D. R., & Jansen, J. D. (2003). Dynamic optimization of waterflooding with smart wells using optimal control theory. SPE Journal, 9(04), SPE-78278–PA. https://doi.org/10.2118/78278-PA
  • Brouwer, D. R., Nævdal, G., Jansen, J. D., Vefring, E. H., & Van Kruijsdijk, C. P. J. W. (2004). Improved reservoir management through optimal control and continuous model updating. SPE Annual Technical Conference and Exhibition, Houston, Texas, September, 2004. https://doi.org/10.2118/90149-MS
  • Cao, F., Luo, H., & Lake, L. W. (2015). Oil-rate forecast by inferring fractional-flow models from field data with koval method combined with the capacitance/resistance model. SPE Reservoir Evaluation & Engineering, 18(04), 534–553. https://doi.org/10.2118/173315-PA
  • Cardoso, M. A., & Durlofsky, L. J. (2010). Use of reduced-order modeling procedures for production optimization. SPE Journal, 15(02), 426–435. https://doi.org/10.2118/119057-PA
  • Dilib, F. A., & Jackson, M. D. (2013). Closed-loop feedback control for production optimization of intelligent wells under uncertainty. SPE Intelligent Energy International, Utrecht, The Netherlands, March, 2012 (pp. SPE-150096–MS). https://doi.org/10.2118/150096-PA
  • Dilib, F. A., Jackson, M. D., & Khairullin, A. Y. (2013). Field production optimization using closed-loop direct feedback control of intelligent wells: Application to the brugge model. SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, September, 2013 (pp. SPE-166384–MS). https://doi.org/10.2118/166384-MS
  • Doren, J. V., Markovinovic, R., & Jansen, J. D. (2004). Reduced-order optimal control of waterflooding using POD. 9th European Conference on the Mathematics of Oil Recovery, Delft University, Netherlands, August, 2004 (pp. cp-9–00084). https://doi.org/10.3997/2214-4609-pdb.9.B009
  • Doren, J. V., Markovinovic, R., & Jansen, J. D. (2006). Use of pod in control of flow through porous media. European Conference on Computational Fluid Dynamics, Delft University of Technology, Netherlands.
  • Essen, G. M., VAn den Hof, P. M. J., & Jansen, J. D. (2012). A two-level strategy to realize life-cycle production optimization in an operational setting. SPE Journal, 18(06), SPE-149736–PA. https://doi.org/10.2118/149736-PA
  • Essen, G. M., Zandvliet, V., VAn den Hof, P. M. J., & Bosgra, O. H. (2006). Robust Optimization of Oil Reservoir Flooding. IEEE COnference on Computer Aided Control System Design, IEEE International Conference on Control Applications, IEEE International Symposium on Intelligent Control, 699–704. https://doi.org/10.1109/CACSD-CCA-ISIC.2006.4776730
  • Fonseca, R. M., Stordal, A. S., Leeuwenburgh, O., VAn den Hof, P. M. J., & Jansen, J. D. (2019). Robust ensemble-based multi-objective optimization. 14th European Conference on the Mathematics of Oil Recovery, September 2014, 8–11. https://doi.org/10.3997/2214-4609.20141895
  • Foss, B., & Jensen, J. P. (2011). Performance analysis for closed-loop reservoir management. SPE Journal, 16(1), 183–190. https://doi.org/10.2118/138891-PA
  • Grebenkin, I. M., & Davies, D. R. (2010). Analysis of the impact of an intelligent well completion on the oil production uncertainty. SPE Russian Oil & Gas Technical Conference and Exhibition, Moscow, Russia, October, 2010 (pp. SPE-136335–MS). https://doi.org/10.2118/136335-MS
  • Grema, A. S., Baba, D., Taura, U. H., Grema, M. B., & Popoola, L. T. (2017). Optimization and nonlinear identification of reservoir waterflooding process. Arid Zone Journal of Engineering, Technology and Environment, 5(1), 449–461. https://doi.org/10.1080/21642583.2017.1378935
  • Grema, A. S., & Cao, Y. (2016). Optimal feedback control of oil reservoir waterflooding process. International Journal of Automation & Computing, 13(1), 73–80. https://doi.org/10.1007/s11633-015-0909-7
  • Grema, A. S., & Cao, Y. (2019). Dynamic Self-Optimizing Control for Uncertain Oil Reservoir Waterflooding Processes. IEEE Transactions on Control Systems Technology, 28(6), 2556–2563. https://doi.org/10.1109/TCST.2019.2934072
  • Grema, A. S., Landa, A. C., & Cao, Y. (2015). Dynamic self optimizing control for oil reservoir waterflooding. IFAC-PapersOnLine, 48(6), 50–55. https://doi.org/10.1016/j.ifacol.2015.08.009
  • Grema, A. S., Mahlon, M. K., Kolo, A. S., & Taura, U. H. (2020). Enhancing oil recovery through waterflooding. Arid Zone Journal of Engineering, Technology and Environment, 16(September), 561–568.
  • Grema, A. S., Mohammed, H. I., Girei, S. A., & Grema, M. B. (2016). Optimization of smart wells using optimal control theory. Journal of Robotic Mechatronics Systems, 1(1), 29–36.
  • Guo, Z., Reynolds, A. C., & Zhao, H. (2017). A physics-based data-driven model for history matching, prediction, and characterization of waterflooding performance. Paper presented at the SPE Reservoir Simulation Conference, Montgomery, Texas, USA, February, 2017 (pp. SPE-182660–MS). https://doi.org/10.2118/182660-MS
  • Hassan, A., & Foss, B. (2013). Optimal Wells Scheduling of a Petroleum Reservoir. European Control Conference (ECC), Zurich, Switzerland, July, 2013. IEEE. https://doi.org/10.23919/ECC.2013.6669831
  • Jansen, J. D. (2011). Adjoint-based optimization of multi-phase flow through porous media – a review. Computers and Fluids, 46(1), 40–51. https://doi.org/10.1016/j.compfluid.2010.09.039
  • Jansen, J., Bosgra, O. H., & Hof, P. M. J. V. D. (2008). Model-based control of multiphase flow in subsurface oil reservoirs. Journal of Process Control, 18(9), 846–855. https://doi.org/10.1016/j.jprocont.2008.06.011
  • Jansen, J. D., Douma, S. D., Brouwer, D. R., VAn den Hof, P. M. J., Bosgra, O. H., & Heemink, A. W. (2009). Closed-Loop reservoir Management. SPE Reservoir Simulation Symposium, The Woodlands, Texas, February, 2009 (pp. SPE-119098–MS). https://doi.org/10.2118/119098-MS
  • Kida, M. M., Sarkinbaka, Z. M., Abubakar, A. M., & Abdul, A. Z. (2021). Neural network based performance evaluation of a waterflooded oil reservoir. International Journal of Recent Engineering Sciences, 8(3), 1–6. https://doi.org/10.14445/23497157/IJRES-V8I3P101
  • Koval, E. J. (1963). A method for predicting the performance of unstable miscible displacement in heterogeneous media. Society of Petroleum Engineers Journal, 3(2), 145–154. https://doi.org/10.2118/450-PA
  • Mamghaderi, A., & Pourafshary, P. (2013). Water flooding performance prediction in layered reservoirs using improved capacitance-resistive model. Journal of Petroleum Science and Engineering, 108, 107–117. https://doi.org/10.1016/j.petrol.2013.06.006
  • Marvin, M. K., Ngulde, A. B., & Abubakar, A. M. (2022). Pattern effect for oil reservoir waterflooding using smart well. Applied Engineering, 6(2), 50–56. https://doi.org/10.11648/j.ae.20220602.13
  • Marvin, M. K., Ngulde, A. B., & Sarkinbaka, A. M. (2023). Waterflood Optimization: Review on Gradient-Ensemble Based Optimizers and Data Driven Proxies. Journal of Engineering Science and Technology Review, 16(4), 1–12. https://doi.org/10.25103/jestr.164.01
  • Mejia, J. A. P., Suarez, J. P. O., & Navarro, J. C. (2014). Optimizing waterflooding process for recovery using nonlinear model predictive control: Case yariguí - cantagallo. SPE Latin America and Caribbean Petroleum Engineering Conference, Maracaibo, Venezuela, May, 2014 (pp. SPE-169447–MS). https://doi.org/10.2118/169447-MS
  • Meum, P., Tondel, P., Godhavn, J., & Aamo, O. M. (2008). Optimization of smart well production through nonlinear model predictive control. Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands, February, 2008 (pp. SPE-112100–MS). https://doi.org/10.2118/112100-MS
  • Mohammad, A. A., Reza, S., Moonyong, L., Tomoaki, K., & Alireza, B. (2015). Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool. Advanced Resource Evolution Science, 1(2), 118–132. https://doi.org/10.1016/j.petlm.2015.06.004
  • Nikolaou, M., Cullick, A. S., & Saputelli, L. (2006). Production optimization — a moving-horizon approach. SPE Journal.
  • Rodriguez, X., Aristizabal, J., Cabrales, S., Gomez, J. M., & Medaglia, L. (2022). Optimal waterflooding management using an embedded predictive analytical model. Journal of Petroleum Science and Engineering, 208, 109419. https://doi.org/10.1016/j.petrol.2021.109419
  • Sarma, P., Aziz, K., & Durlofsky, L. J. (2005). Implementation of adjoint solution for optimal control of smart wells. SPE Reservoir Simulation Symposium, The Woodlands, Texas, January, 2005 (pp. SPE-92864–MS). https://doi.org/10.2118/92864-MS
  • Siraj, M. M., Van Den Hof, P. M. J., & Jansen, J. D. (2017). An adaptive robust optimization scheme for water-flooding optimization in oil reservoirs using residual analysis. IFAC-PapersOnLine, 50(1), 11275–11280. https://doi.org/10.1016/j.ifacol.2017.08.1632
  • Sun, X., & Xu, M. (2017). Optimal control of water flooding reservoir using proper orthogonal decomposition. Journal of Computational and Applied Mathematics, 320, 120–137. https://doi.org/10.1016/j.cam.2017.01.020
  • Volcker, C., Bagterp, J. J., Grove, P. T., & Stenby, H. E. (2011). NMPC for Oil Reservoir Production Optimization. Computer Aided Chemical Engineering, 29, 1849–1853. https://doi.org/10.1016/B978-0-444-54298-4.50148-3
  • Wang, D., Li, Y., Zhang, J., Wei, C., Jiao, Y., & Wang, Q. (2019). Improved CRM model for inter-well connectivity estimation and production optimization: Case study for karst reservoirs. Energies, 12(5), 816. https://doi.org/10.3390/en12050816
  • Wellano, J., Pinto, O., Maria, S., Afonso, B., & Brito, R. (2019). Robust optimization formulations for waterflooding management under geological uncertainties. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41(11), 1–16. https://doi.org/10.1007/s40430-019-1970-x
  • Zandvliet, M. J., Bosgra, O. H., Jansen, J. D., Van den Hof, P. M. J., & Kraaijevanger, J. F. B. M. (2007). Bang-bang control and singular arcs in reservoir flooding. Journal of Petroleum Science and Engineering, 58(1–2), 186–200. https://doi.org/10.1016/j.petrol.2006.12.008
  • Zhang, Z., Li, H., & Zhang, D. (2015). Water flooding performance prediction by multi-layer capacitance-resistive models combined with the ensemble Kalman filter. Journal of Petroleum Science and Engineering, 127, 1–19. https://doi.org/10.1016/j.petrol.2015.01.020
  • Zhang, Z., Li, H., & Zhang, D. (2017). Reservoir characterization and production optimization using the ensemble based optimization method and multi-layer capacitance resistive models. Journal of Petroleum Science and Engineering, 156, 633–653. https://doi.org/10.1016/j.petrol.2017.06.020
  • Zhao, H., Li, Y., Cui, S., Shang, G., Albert, C., Guo, Z., & Li, H. A. (2016). History matching and production optimization of water flooding based on a data-driven interwell numerical simulation model. Journal of Natural Gas Science & Engineering, 31, 48–66. https://doi.org/10.1016/j.jngse.2016.02.043
  • Zhao, H., Zhijiang, H., Zhang, X., Sun, H., Cao, L., & Reynolds, A. C. (2015). INSIM: A data driven model for history matching and prediction for waterflooding monitoring and management with a field application. SPE Journal. https://SPE-173213-MS

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.