Review of Reservoir Closed-Loop Optimization Control Methods

Full Text

Download the full text PDF for viewing and using it according to the license of this paper.

References

1. Mohanty, K. K., Patil, S., & Kovscek, A. R. (2022). The digital transformation in upstream oil and gas: A review of intelligent oil fields and the application of data-driven analytics. SPE Journal, 27(1), 123–145. https://doi.org/10.2118/208552-PA

2. Wang, Y., Sun, H., & Oliver, D. S. (2023). Real-time production optimization of oil reservoirs using a machine learning-based closed-loop framework. Journal of Petroleum Science and Engineering, 220(Part A), 111182. https://doi.org/10.1016/j.petrol.2022.111182

3. Xu, L., Chen, F. F., & King, M. J. (2023). Digital twin for intelligent oil field development: A review of concepts, applications, and challenges. Energy Reports, 9, 2967–2981. https://doi.org/10.1016/j.egyr.2023.01.120

4. Jansen, J. D., Brouwer, D. R., Douma, S. G., & Van den Hof, P. M. J. (2021). Closed-loop reservoir management: An introductory review. SPE Journal, 26(3), 1357–1381. https://doi.org/10.2118/205356-PA

5. Chen, Y., Oliver, D. S., & Zhang, D. (2022). Efficient ensemble-based closed-loop production optimization. SPE Journal, 27(1), 688–710. https://doi.org/10.2118/209219-PA
6. Fonseca, R. M., Kahrobaei, S., Van Essen, G. M., & Jansen, J. D. (2023). From history matching to production optimization: A consistent closed-loop framework using adjoint-based methods. Computational Geosciences, 27(2), 317–335. https://doi.org/10.1007/s10596-022-10187-x
7. Liu, X., Zhang, K., & Reynolds, A. C. (2022). Accelerating closed-loop reservoir management with ensemble smoother and deep-learning-based proxy models. Journal of Petroleum Science and Engineering, 216(Part B), 110805. https://doi.org/10.1016/j.petrol.2022.110805
8. Tang, M., Liu, Y., & Durlofsky, L. J. (2023). A deep-learning-based closed-loop reservoir management framework that directly maps from production history to optimal future controls. SPE Journal, 28(4), 1947–1966. https://doi.org/10.2118/214646-PA
9. Emerick, A. A. (2022). Ensemble smoother with multiple data assimilation: Current status and future directions. Computational Geosciences, 26(6), 1493–1515. https://doi.org/10.1007/s10596-022-10170-6
10. Wang, H., & Li, G. (2023). Deep reinforcement learning for production optimization in reservoir management: Framework and application. Journal of Petroleum Science and Engineering, 220(Part A), 111235. https://doi.org/10.1016/j.petrol.2022.111235
11. Tan, T. B., & Kalogerakis, N. (1991). A fully implicit, three-dimensional, three-phase simulator with automatic history-matching capability [Conference session]. Society of Petroleum Engineers. https://doi.org/10.2118/21205-MS
12. Zhang, F., & Reynolds, A. C. (2002). Optimization algorithms for automatic history matching of production data [Conference session]. Proceedings of the European Conference on the Mathematics of Oil Recovery.
13. Gavalas, G. R., Shah, P. C., & Seinfeld, J. H. (1976). Reservoir history matching by Bayesian estimation. Society of Petroleum Engineers Journal, 16(6), 337–350. https://doi.org/10.2118/5740-PA
14. Gao, G., & Reynolds, A. C. (2004). An improved implementation of the LBFGS algorithm for automatic history matching. SPE Journal, 9(3), 316–326. https://doi.org/10.2118/90058-MS
15. Tavakoli, R., & Reynolds, A. C. (2010). History matching with parameterization based on the SVD of a dimensionless sensitivity matrix. SPE Journal, 15(2), 495–508. https://doi.org/10.2118/118950-PA
16. Sarma, P., Durlofsky, L. J., & Aziz, K. (2012). A new approach to automatic history matching using kernel PCA [Conference session]. Society of Petroleum Engineers. https://doi.org/10.2118/106176-MS
17. Jafarpour, B., & McLaughlin, D. B. (2008). History matching with an ensemble Kalman filter and discrete cosine parameterization. Computational Geosciences, 12(2), 227–244. https://doi.org/10.1007/s10596-008-9080-3
18. Jin, J., Li, S., Li, J., & Ding, F. (2024). Geological model calibration based on gradual deformation and connectivity function. Scientific Reports, 14(1), 29395. https://doi.org/10.1038/s41598-024-80363-9
19. Marteau, B., Ding, D. Y., & Dumas, L. (2014). A generalization of the local gradual deformation method using domain parameterization. Computers & Geosciences, 72, 233–243. https://doi.org/10.1016/j.cageo.2014.07.016
20. Wang, C., Li, G., & Reynolds, A. C. (2007). Production optimization in the context of closed-loop reservoir management [Conference session]. Society of Petroleum Engineers. https://doi.org/10.2118/109805-MS
21. Li, G., & Reynolds, A. C. (2011). Uncertainty quantification of reservoir performance predictions using a stochastic optimization algorithm. Computational Geosciences, 15(3), 451–462. https://doi.org/10.1007/s10596-010-9213-3
22. Marsily, G., Lavedan, C., Boucher, M., & Fasanino, G. (1984). Interpretation of interference tests in a well field using geostatistical techniques to fit the permeability distribution in a reservoir model. In G. Verly (Ed.), Geostatistics for Natural Resources Characterization (pp. 831–849). Springer.
23. Abdassah, D., Mucharam, L., Soengkowo, I., Trikoranto, H., & Sumantri, R. (1996). Coupling seismic data with simulated annealing method improves reservoir characterization [Conference session]. Society of Petroleum Engineers. https://doi.org/10.2118/36968-MS
24. Romero, C. E., Carter, J. N., Gringarten, A. C., & Zimmerman, R. W. (2000). A modified genetic algorithm for reservoir characterization [Conference session]. Society of Petroleum Engineers. https://doi.org/10.2118/64765-MS
25. Sen, M. K., Datta-Gupta, A., Stoffa, P. L., Lake, L. W., & Pope, G. A. (1995). Stochastic reservoir modeling using simulated annealing and genetic algorithm [Conference session]. Society of Petroleum Engineers. https://doi.org/10.2118/24754-PA
26. Evensen, G. (2003). The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343–367. https://doi.org/10.1007/s10236-003-0036-9
27. Aanonsen, S. I., Nævdal, G., Oliver, D. S., Reynolds, A. C., & Vallès, B. (2009). The ensemble Kalman filter in reservoir engineering—a review. SPE Journal, 14(3), 393–412. https://doi.org/10.2118/117274-PA
28. Sun, M., Luo, Q., Kong, Z., Guo, M., Liu, M., & Qian, J. (2024). A novel approach for estimating hydraulic conductivity of non-Gaussian aquifer. Hydrogeology & Engineering Geology, 51(3), 23–33. https://doi.org/10.16030/j.cnki.issn.1000-3665.202308022
29. Reynolds, A. C., He, N., & Oliver, D. S. (1999). Reducing uncertainty in geostatistical description with well-testing pressure data. In Reservoir Characterization: Recent Advances (pp. 149–162). American Association of Petroleum Geologists.
30. Liu, N., & Oliver, D. S. (2004). Evaluation of Monte Carlo methods for assessing uncertainty. SPE Journal, 9(2), 188–195. https://doi.org/10.2118/84936-PA
31. Ripley, B. D. (2009). Stochastic simulation. Wiley.
32. Guo, Z., & Reynolds, A. C. (2018). Robust life-cycle production optimization with a support-vector-regression proxy. SPE Journal, 23(6), 2409–2427. https://doi.org/10.2118/182645-PA
33. Cao, Z., Song, Q., Xing, K., Han, J., & Qie, D. (2025). A novel multihead attention-enhanced convolutional neural network–long short-term memory encoder–decoder for oil production forecasting. SPE Journal. https://doi.org/10.2118/223940-PA
34. Wu, G., Wang, F., Cheng, S., & Zhang, C. (2022). A single well production forecasting model of reservoir based on conditional generative adversarial net [Chinese journal article with English title]. Chinese Journal of Computational Physics, 39(4), 465–478. https://doi.org/10.19596/j.cnki.1001-246x.8480
35. Zhou, Y., Xu, Y., Rao, X., Hu, Y., Liu, D., & Zhao, H. (2021). Artificial neural network–based proxy model for fast performances’ forecast and inverse schedule design of steam-flooding reservoirs. Mathematical Problems in Engineering, 2021, Article 5527259. https://doi.org/10.1155/2021/5527259
36. Sun, W., & Durlofsky, L. J. (2017). A new data-space inversion procedure for efficient uncertainty quantification in subsurface flow problems. Mathematical Geosciences, 49(6), 679–715. https://doi.org/10.1007/s11004-016-9671-9
37. Sun, W., Hui, M., & Durlofsky, L. J. (2017). Production forecasting and uncertainty quantification for naturally fractured reservoirs using a new data-space inversion procedure. Computational Geosciences, 21(5), 1443–1458. https://doi.org/10.1007/s10596-017-9675-7
38. Sun, W., & Durlofsky, L. J. (2019). Data-space approaches for uncertainty quantification of CO2 plume location in geological carbon storage. Advances in Water Resources, 128, 234–255. https://doi.org/10.1016/j.advwatres.2019.04.014
39. Liu, D., Rao, X., Zhao, H., Xu, Y., & Gong, R. (2021). An improved data space inversion method to predict reservoir state fields via observed production data. Petroleum Science, 18(4), 1127–1142. https://doi.org/10.1016/j.petsci.2021.07.003
40. Brouwer, D. R., & Jansen, J. D. (2004). Dynamic optimization of water flooding with smart wells using optimal control theory. SPE Journal, 9(4), 391–402. https://doi.org/10.2118/78278-PA
41. Ibiam, E. (2021). Numerical simulation and optimisation of polymer flooding in a heterogeneous reservoir: constrained versus unconstrained optimisation [Doctoral dissertation, Heriot-Watt University]. Heriot-Watt University Research Portal. https://doi.org/10.17861/ea00ac27-40f8-4e9e-bc6c-02f5fb4b3a41
42. Zhang, K. (2008). Research on the theory of reservoir dynamic real-time optimization [Doctoral dissertation, China University of Petroleum]. (Original work published in Chinese)
43. Liu, Y., Zhao, W., Zhang, N., Lv, D., & Zhang, S. (2025). Gradient-free distributed online optimization in networks. Control Theory and Technology. https://doi.org/10.1007/s11768-025-00242-0
44. Chen, Y., & Oliver, D. S. (2009). Efficient ensemble-based closed-loop production optimization. SPE Journal, 14(4), 634–645. https://doi.org/10.2118/112257-PA
45. Silva, A. S., & Schiozer, D. J. (2025). Balancing conflicting objectives in pre-salt reservoir development: A robust multi-objective optimization framework. Unconventional Resources, 5, 100130. https://doi.org/10.1016/j.uncres.2024.100130
46. Brum, A., Coelho, G., Santos, A. A., & Schiozer, D. J. (2025). Estimation of distribution algorithms for well placement optimization in petroleum fields. Computational Geosciences, 29(1), 2. https://doi.org/10.1007/s10596-024-10331-9
47. Wu, S., Liu, L., Wu, Y., Zhang, Y., Xie, W., & Jiang, H. (2025). Ant colony optimization for accelerated pathway identification in connection element method reservoir models: A fast-track solution for large-scale simulations. Processes, 13(2), 404. https://doi.org/10.3390/pr13020404
48. Danes, L. H., Avansi, G. D., & Schiozer, D. J. (2025). Using surrogates as faster objective function estimators for oil production strategy optimization in a real-case reservoir. SPE Journal. https://doi.org/10.2118/225411-PA
49. Yousefzadeh, R., Kazemi, A., Al-Hmouz, R., & Al-Moosawi, I. (2025). Determination of optimal oil well placement using deep learning under geological uncertainty. Geoenergy Science and Engineering, 246, 213621. https://doi.org/10.1016/j.geoen.2025.213621
50. Foroud, T., Baradaran, A., & Seifi, A. (2018). A comparative evaluation of global search algorithms in black box optimization of oil production: A case study on Brugge field. Journal of Petroleum Science and Engineering, 167, 131–151. https://doi.org/10.1016/j.petrol.2018.04.015
51. Golzari, A., Haghighat, S., & Jamshidi, S. (2015). Development of an adaptive surrogate model for production optimization. Journal of Petroleum Science and Engineering, 133, 677–688. https://doi.org/10.1016/j.petrol.2015.07.016
52. Chen, G., Zhang, K., Zhang, L., Xue, X., & Li, C. (2020). Global and local surrogate-model assisted differential evolution for waterflooding production optimization. SPE Journal, 25(1), 105–118. https://doi.org/10.2118/199340-PA
53. Jiang, S. (2018). Data-space inversion with variable well controls in the prediction period [Master's thesis, Stanford University].
54. Jiang, S., Sun, W., & Durlofsky, L. J. (2020). A data-space inversion procedure for well control optimization and closed-loop reservoir management. Computational Geosciences, 24(1), 361–379. https://doi.org/10.1007/s10596-019-09920-w
55. Kim, Y., Min, B., Sun, A., Ren, B., & Jeong, H. (2023). Comparative study of data-driven forecasting methods for reservoir production using data-space inversion and deep learning. Fuel, 332(Part 2), 127067. https://doi.org/10.1016/j.fuel.2022.127067