Impact of Grid Refinement on a Non-Conventional Mesh Structure: A Case Study with INSIM-FT
Resumo
To support decision-making, the oil and gas industry relies on flow simulators to evaluate multiple production scenarios. This demands simulation tools that are both accurate and computationally efficient. INSIM-FT (Interwell Numeric Simulation Model with Front-Tracking) is a hybrid flow simulator that combines physical modeling with data-driven components, using a non-conventional mesh built through Mitchell’s Best Candidate algorithm and Delaunay Triangulation. Although finer meshes are expected to improve spatial representation, they also increase computational costs and may interfere with the performance of data-driven elements. This trade-off highlights the need to assess the influence of the number of imaginary nodes used in INSIM-FT. This work investigates how mesh configuration impacts the accuracy and performance of the simulator through a case study of a heterogeneous reservoir. Considering randomly generated models whose parameters are adjusted by the datadriven component, the results show that mesh refinement affects the spread of the adjusted prior models around the mean. Nevertheless, the mean of the simulations remains reasonably close to the observed data in all cases.Referências
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A. A. Emerick, “Investigation on principal component analysis parameterizations for history matching channelized facies models with ensemble-based data assimilation,” Mathematical Geosciences, vol. 49, no. 1, pp. 85–120, 2017.
M. Grave, E. da Silva Castro, S. Pesco, A. B. B. Junior, L. da Silva Gasparini, R. G. Cavalcante, R. R. da Costa Alves, and J. R. P. Rodrigues, “A mass conservative insim-ft-3d physics-based data-driven oil-water reservoir simulator,” Geoenergy Science and Engineering, vol. 241, p. 213102, 2024.
Y. Zhao, Parameterization and Data Assimilation for Complex Geological Reservoirs. The University of Tulsa, 2019.
R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International journal of forecasting, vol. 22, no. 4, pp. 679–688, 2006.
A. François, F. Maubeuge, E. Tourte, P. Montagnier, and O. Dubrule, “High-performance computing: Towards a safer, faster and sharper exploration & production,” in World Petroleum Congress. WPC, 2014, p. D033S005R002.
C. Maschio, C. P. V. de Carvalho, and D. J. Schiozer, “A new methodology to reduce uncertainties in reservoir simulation models using observed data and sampling techniques,” Journal of Petroleum Science and Engineering, vol. 72, no. 1-2, pp. 110–119, 2010.
S. Afshari, B. Aminshahidy, and M. R. Pishvaie, “Application of an improved harmony search algorithm in well placement optimization using streamline simulation,” Journal of Petroleum Science and Engineering, vol. 78, no. 3-4, pp. 664–678, 2011.
P. Sarma, K. Aziz, and L. J. Durlofsky, “Implementation of adjoint solution for optimal control of smart wells,” in SPE Reservoir Simulation Conference. SPE, 2005, pp. SPE–92 864.
G. Van Essen, M. Zandvliet, P. Van den Hof, O. Bosgra, and J.-D. Jansen, “Robust waterflooding optimization of multiple geological scenarios,” Spe Journal, vol. 14, no. 01, pp. 202–210, 2009.
K. J. Heffer, R. J. Fox, C. A. McGill, and N. C. Koutsabeloulis, “Novel techniques show links between reservoir flow directionality, earth stress, fault structure and geomechanical changes in mature waterfloods.” SPE Journal, vol. 2, no. 02, pp. 91–98, 1997.
F. Jansen and M. Kelkar, “Non-stationary estimation of reservoir properties using production data,” in SPE Annual Technical Conference and Exhibition. SPE, 1997, pp. SPE–38 729.
B. T. Refunjol, “Reservoir characterization of north buck draw field based on tracer response and production/injection analysis,” Ph.D. dissertation, The University of Texas at Austin, 1996.
H. Zhao, Z. Kang, X. Zhang, H. Sun, L. Cao, and A. C. Reynolds, “Insim: A data-driven model for history matching and prediction for waterflooding monitoring and management with a field application,” in SPE Reservoir Simulation Conference. SPE, 2015, p. D021S007R004.
——, “A physics-based data-driven numerical model for reservoir history matching and prediction with a field application,” SPE Journal, vol. 21, no. 06, pp. 2175–2194, 2016.
S. E. Buckley and M. Leverett, “Mechanism of fluid displacement in sands,” Transactions of the AIME, vol. 146, no. 01, pp. 107–116, 1942.
H. Zhao, W. Liu, X. Rao, G. Sheng, H. A. Li, Z. Guo, D. Liu, and L. Cao, “Insim-fpt-3d: A data-driven model for history matching, water-breakthrough prediction and well-connectivity characterization in three-dimensional reservoirs,” in SPE Reservoir Simulation Conference. SPE, 2021, p. D011S006R004.
Y. Li and M. Onur, “Insim-bhp: A physics-based data-driven reservoir model for history matching and forecasting with bottomhole pressure and production rate data under waterflooding,” Journal of Computational Physics, vol. 473, p. 111714, 2023. [Online]. Available: [link]
Z. Guo and A. C. Reynolds, “Insim-ft in three-dimensions with gravity,” Journal of Computational Physics, vol. 380, pp. 143–169, 2019. [Online]. Available: [link]
Z. Guo, History Matching, Prediction and Production Optimization with a Physics-Based Data-Driven Model. The University of Tulsa, 2018.
D. P. Mitchell, “Spectrally optimal sampling for distribution ray tracing,” in Proceedings of the 18th annual conference on Computer graphics and interactive techniques, 1991, pp. 157–164.
H. Edelsbrunner, T. S. Tan, and R. Waupotitsch, “An o (n 2log n) time algorithm for the minmax angle triangulation,” in Proceedings of the sixth annual symposium on Computational geometry, 1990, pp. 44–52.
D. Kaviani, P. P. Valkó, and J. L. Jensen, “Application of the multiwell productivity index-based method to evaluate interwell connectivity,” in SPE Improved Oil Recovery Conference. SPE, 2010, pp. SPE–129 965.
G. Ren, Z. Wang, Y. Lin, T. Onishi, X. Guan, and X.-H. Wen, “A fast history-matching and optimization tool and its application to a full field with more than 1,000 wells,” SPE Journal, vol. 29, no. 10, pp. 5538–5558, 10 2024. [Online]. DOI: 10.2118/212188-PA
A. A. Emerick and A. C. Reynolds, “History matching time-lapse seismic data using the ensemble kalman filter with multiple data assimilations,” Computational Geosciences, vol. 16, pp. 639–659, 2012.
——, “Ensemble smoother with multiple data assimilation,” Computers & Geosciences, vol. 55, pp. 3–15, 2013.
——, “History-matching production and seismic data in a real field case using the ensemble smoother with multiple data assimilation,” in SPE Reservoir Simulation Conference. SPE, 2013, pp. SPE–163 675.
A. Emerick, Ensemble Data Assimilation Applied to Geological Reservoir Models, 05 2025.
C. M. Dafermos, “Polygonal approximations of solutions of the initial value problem for a conservation law,” Journal of Mathematical Analysis and Applications, vol. 38, no. 1, pp. 33–41, 1972. [Online]. Available: [link]
J. O. Langseth, N. H. Risebro, and A. Tveito, “A conservative front tracking scheme for 1d hyperbolic conservation laws,” in Nonlinear Hyperbolic Problems: Theoretical, Applied, and Computational Aspects: Proceedings of the Fourth International Conference on Hyperbolic Problems, Taormina, Italy, April 3 to 8, 1992. Springer, 1993, pp. 385–392.
M. De Berg, Computational geometry: algorithms and applications. Springer Science & Business Media, 2000.
A. A. Emerick, “Investigation on principal component analysis parameterizations for history matching channelized facies models with ensemble-based data assimilation,” Mathematical Geosciences, vol. 49, no. 1, pp. 85–120, 2017.
M. Grave, E. da Silva Castro, S. Pesco, A. B. B. Junior, L. da Silva Gasparini, R. G. Cavalcante, R. R. da Costa Alves, and J. R. P. Rodrigues, “A mass conservative insim-ft-3d physics-based data-driven oil-water reservoir simulator,” Geoenergy Science and Engineering, vol. 241, p. 213102, 2024.
Y. Zhao, Parameterization and Data Assimilation for Complex Geological Reservoirs. The University of Tulsa, 2019.
R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International journal of forecasting, vol. 22, no. 4, pp. 679–688, 2006.
Publicado
30/09/2025
Como Citar
LÓPEZ, Dimary Moreno; TEIXEIRA, João; PESCO, Sinesio; B. JUNIOR, Abelardo B.; RODRIGUES, José R. P.; ALVES, Regina; CAVALCANTE, Rodrigo Gusmão; GRAVE, Malú.
Impact of Grid Refinement on a Non-Conventional Mesh Structure: A Case Study with INSIM-FT. In: WORKSHOP DE APLICAÇÕES INDUSTRIAIS - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 38. , 2025, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 317-324.
