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Pattern Effect for Oil Reservoir Waterflooding Using Smart Well

Waterflooding is a primary enhanced oil recovery involving the injection of water into an oil-gas rich reservoir to increase production capacity. Waterflooding is one of the most used enhanced oil recovery technique due to the fact that water is readily available and cheap to maintain. However, with the efficacy of implementing waterflooding recovery technique, only about 35% of the original oil in place (OOIP) is produced. This research is aimed at investigating the effect of placement pattern for non-conventional or smart wells. Comparison is made with respect to previous study where which conventional wells are used. Three cases were investigated on the basis of recovery and complexities in field development. It was observed from this study that conventional wells are not a good candidate for oil well productivity as compared to non-conventional (smart) wells. Conventional wells also pose a limitation to the economic value of the reservoir due to poor well contact. The first, second and third case recorded an NPV of $7.5 trillion, $7.59 trillion and $8.81 trillion respectively. Implementing smart wells also curtailed an early water breakthrough by about 70%. An average gain of 99.7% was also recorded for all cases as against previous study. These results indicated the efficiency of implementing smart wells over conventional wells.

Smart Wells, Waterflooding, Net Present Value, Production Rate, Five-Spot Pattern

APA Style

Mahlon Kida Marvin, Aliyu Buba Ngulde, Abdulhalim Musa Abubakar. (2022). Pattern Effect for Oil Reservoir Waterflooding Using Smart Well. Applied Engineering, 6(2), 50-56.

ACS Style

Mahlon Kida Marvin; Aliyu Buba Ngulde; Abdulhalim Musa Abubakar. Pattern Effect for Oil Reservoir Waterflooding Using Smart Well. Appl. Eng. 2022, 6(2), 50-56. doi: 10.11648/

AMA Style

Mahlon Kida Marvin, Aliyu Buba Ngulde, Abdulhalim Musa Abubakar. Pattern Effect for Oil Reservoir Waterflooding Using Smart Well. Appl Eng. 2022;6(2):50-56. doi: 10.11648/

Copyright © 2022 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. A. S. Grema and Y. Cao, “Dynamic Self-Optimizing Control for Uncertain Oil Reservoir Waterflooding Processes,” IEEE Trans. Control Syst. Technol., vol. 28, no. 6, pp. 2556–2563, 2020, doi: 10.1109/TCST.2019.2934072.
2. A. S. Grema, M. K. Mahlon, U. H. Taura, and A. S. Kolo, “Enhancing Oil Recovery through Waterflooding,” Arid Zo. J. Eng. Technol. Environ., vol. 16, no. 3, pp. 561–568, 2020.
3. Z. O. U. Caineng, Y. Zhi, Z. Guosheng, H. O. U. Lianhua, Z. H. U. Rukai, and T. A. O. Shizhen, “Conventional and unconventional petroleum ‘ orderly accumulation ’: Concept and practical significance,” Pet. Explor. Dev., vol. 41, no. 1, pp. 14–30, 2014, doi: 10.1016/S1876-3804(14)60002-1.
4. J. Udy et al., “Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms,” Process. MDPI, vol. 5, no. 34, p. 25, 2017, doi: 10.3390/pr5030034.
5. W. Bangerth, H. Klie, M. F. Wheeler, P. L. Stoffa, and M. K. Sen, “On optimization algorithms for the reservoir oil well placement problem,” Comput Geosci, vol. 10, pp. 303–319, 2006, doi: 10.1007/s10596-006-9025-7.
6. C. Wang, G. Li, and A. C. Reynolds, “Optimal Well Placement for Production Optimization,” in SPE Eastern Regional Meeting, 2007, pp. 11–14.
7. F. Forouzanfar, G. Li, and A. C. Reynolds, “A two-stage well placement optimization method based on adjoint gradient,” in 2010 SPE Annual Technical Conference and Exhibition, 2010, no. September, pp. 20–22.
8. F. Forouzanfar and A. C. Reynolds, “Joint optimization of number of wells, well locations and controls using a gradient-based algorithm,” Chem. Eng. Res. Des., no. September 2012, pp. 1–14, 2013, doi: 10.1016/j.cherd.2013.11.006.
9. P. Sarma and W. H. Chen, “Efficient Well Placement Optimization with Gradient- Based Algorithms and Adjoint Models,” in SPE Intelligent Energy Conference and Exhibition, 2008, p. 18.
10. M. A. Al Dossary and H. Nasrabadi, “Well placement optimization using imperialist competitive algorithm,” J. Pet. Sci. Eng., vol. 147, pp. 237–248, 2016, doi: 10.1016/j.petrol.2016.06.017.
11. X. Sun and M. Xu, “Optimal control of water flooding reservoir using proper orthogonal decomposition,” J. Comput. Appl. Math., vol. 320, pp. 120–137, 2017, doi: 10.1016/
12. A. Centilmen, T. Ertekin, and A. S. Grader, “Applications of Neural Networks in Multiwell Field Development,” 1999.
13. M. A. Dada, M. Mellal, and A. Makhloufi, “A field development strategy for the joint optimization of flow allocations, well placements and well trajectories,” Energy Explor. Exploit., vol. 39, no. 1, pp. 502–527, 2021, doi: 10.1177/0144598720974425.
14. I. Jang, S. Oh, Y. Kim, C. Park, and H. Kang, “Well-placement optimisation using sequential artificial neural networks,” Energy Explor. Exploit., vol. 36, no. 3, pp. 433–449, 2018, doi: 10.1177/0144598717729490.
15. X. Xiong and K. J. Lee, “Data-driven modeling to optimize the injection well placement for waterflooding in heterogeneous reservoirs applying artificial neural networks and reducing observation cost,” Energy Explor. Exploit., vol. 38, no. 6, pp. 2413–2435, 2020, doi: 10.1177/0144598720927470.
16. L. Tang et al., “Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network,” Geofluids, p. 15, 2021, doi:
17. C. N. W. Shang, A. G. Jahanbani, and A. N. Menad, “Application of nature‑inspired algorithms and artificial neural network in waterflooding well control optimization,” J. Pet. Explor. Prod. Technol., vol. 10, 2021, doi: 10.1007/s13202-021-01199-x.
18. M. M. Kida, Z. M. Sarkinbaka, A. M. Abubakar, and A. Z. Abdul, “Neural Network Based Performance Evaluation of a Waterflooded Oil Reservoir,” Int. J. Recent Eng. Sci., vol. 8, no. 3, pp. 1–6, 2021, doi: 10.14445/23497157/IJRES-V8I3P101.
19. B. H. Min, C. Park, J. M. Kang, H. J. Park, and I. S. Jang, “Optimal Well Placement Based on Artificial Neural Network Incorporating the Productivity Potential,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 33, no. 18, pp. 37–41, 2011, doi: 10.1080/15567030903468569.
20. K. F. Bou-hamdan and A. H. Abbas, “Utilizing Ultrasonic Waves in the Investigation of Contact Stresses, Areas, and Embedment of Spheres in Manufactured Materials Replicating Proppants and Brittle Rocks,” Arab. J. Sci. Eng., 2021, doi: 10.1007/s13369-021-06409-6.
21. Z. Xiao et al., “A review of development methods and EOR technologies for carbonate reservoirs,” Pet. Sci., vol. 17, no. 4, pp. 990–1013, 2020, doi: 10.1007/s12182-020-00467-5.
22. K. F. B. Hamdan, R. Harkouss, and H. A. Chakra, “An Overview Of Extended Reach Drilling : Focus on design consideration and drag analysis,” in International Mediterranean Gas and Oil Conference (MedGo), 2015, pp. 1–4.
23. J. D. Jansen, S. D. Douma, D. R. Brouwer, P. M. J. VAn den Hof, O. H. Bosgra, and A. W. Heemink, “Closed-loop reservoir management,” in SPE reservoir simulation symposium, 2009, p. 18.
24. U. R. Chaudhuri, Fundamentals of Petroleum and Petrochemical Engineering. Taylor & Francis, 2011.
25. D. R. Brouwer, G. Nævdal, J. D. Jansen, E. H. Vefring, and C. P. J. W. Van Kruijsdijk, “Improved Reservoir Management Through Optimal Control and Continuous Model Updating,” SPE J., p. 11, 2004.