Channeling paths identification and mitigation by using integrated profile control and flooding based on meshless connection element method

  • Wei Yong orcid

    State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China; CNOOC Research Institute Co. Ltd., Beijing 100028, China
  • Zhijie Wei

    State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China; CNOOC Research Institute Co. Ltd., Beijing 100028, China
  • Jian Zhang

    State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China; CNOOC Research Institute Co. Ltd., Beijing 100028, China
  • Wensheng Zhou

    State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China; CNOOC Research Institute Co. Ltd., Beijing 100028, China
  • Yuyang Liu

    State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China; CNOOC Research Institute Co. Ltd., Beijing 100028, China
  • Wentao Zhan orcid

    Western Research Institute, Yangtze University, Karamay 834000, China; State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Yangtze University, Wuhan 430100, China
  • Wei Liu orcid

    Western Research Institute, Yangtze University, Karamay 834000, China; State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Yangtze University, Wuhan 430100, China
  • Yixin Zhang orcid

    State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Yangtze University, Wuhan 430100, China
  • Jiaxu Mei orcid

    State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Yangtze University, Wuhan 430100, China
Article ID: 4327
DOI: https://doi.org/10.59400/adecp4327
Keywords: meshless connection element method; integrated profile control; identification of channeling paths

Abstract

Addressing the challenge of quantitatively identifying deep thief zones in mature oilfields during the high water-cut stage, this study proposes a robust quantitative characterization model for thief channels based on the non-Euclidean, meshless Connection Element Method (CEM) to directly guide in-depth fluid diversion and integrated profile control and flooding treatments through automated flow path tracking rooted in a directed-graph depth-first search algorithm. To systematically capture the complex subsurface topological network, a comprehensive multi-parameter connectivity parameter system was constructed by integrating key dynamic indicators, including connection conductivity, connection volume, and path splitting coefficients. By dynamically coupling these parameters with the field-wide Lorentz coefficient, a dimensionless channeling factor was defined to establish a rigorous four-level quantitative standard—ranging from extreme channeling to matrix seepage—thereby successfully advancing preferential pathway evaluation from qualitative inference to spatial grading and precise localization. Quantitative validation against conventional commercial grid-based compositional simulators demonstrates the superior fidelity and performance forecasting efficiency of the proposed method: the CEM framework achieves an exceptionally accurate water-cut prediction Root Mean Square Error (RMSE) of approximately 3.8%. Crucially, by abstracting continuous domains into streamlined networks, it drastically compresses structural degrees of freedom, successfully accelerating the operational execution runtime from 7.3 s to a mere 1.6 s. Ultimately, this work provides a computationally highly efficient, physically sound novel approach for the reliable mapping and graded quantification of deep dominant channeling pathways in mature heterogeneous oilfields.

Published
2026-07-17
How to Cite
Yong, W., Wei, Z., Zhang, J., Zhou, W., Liu, Y., Zhan, W., Liu, W., Zhang, Y., & Mei, J. (2026). Channeling paths identification and mitigation by using integrated profile control and flooding based on meshless connection element method. Advances in Differential Equations and Control Processes, 33(3). https://doi.org/10.59400/adecp4327
Issue
Vol. 33 No. 3 (2026): In Progress
Section
Article

Copyright (c) 2026 Wei Yong, Zhijie Wei, Jian Zhang, Wensheng Zhou, Yuyang Liu, Wentao Zhan, Wei Liu, Yixin Zhang, Jiaxu Mei

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Han D. Study on Accurate Prediction of Remaining Oil Relative Enrichment Zones to Enhance Waterflooding Recovery in Oilfields. Acta Petrolei Sinica, 2007; 28: 73–78.

[2]Zhang S, Yu H, Wang Y, et al. Research on Displacement Technology for a Binary Heterogeneous System in Fractured-Vuggy Reservoirs. Journal of Energy Engineering. 2026; 152(3): 04026018. doi: 10.1061/JLEED9.EYENG-6633

[3]Nie X, Chen J, Cao Y, et al. Investigation on Plugging and Profile Control of Polymer Microspheres as a Displacement Fluid in Enhanced Oil Recovery. Polymers. 2019; 11(12): 1993. doi: 10.3390/polym11121993

[4]Tong K, Song W, Chen H, et al. Automatic History Matching Method and Application of Artificial Intelligence for Fractured-Porous Carbonate Reservoirs. Processes. 2024; 12(12): 2634. doi: 10.3390/pr12122634

[5]Kabir CS, Ismadi D, Fountain S. Estimating in-place volume and reservoir connectivity with real-time and periodic surveillance data. Journal of Petroleum Science and Engineering. 2011; 78(2): 258–266. doi: 10.1016/j.petrol.2011.05.017

[6]Nagao M, Datta-Gupta A. Physics Informed Machine Learning for Reservoir Connectivity Identification and Production Forecastingfor CO2-EOR. In: Proceedings of the SPE Annual Technical Conference and Exhibition; 20 September 2024; New Orleans, LA, USA. doi: 10.2118/221057-MS

[7]England WA. The organic geochemistry of petroleum reservoirs. Organic Geochemistry. 1990; 16(1–3): 415–425. doi: 10.1016/0146-6380(90)90058-8

[8]Dinh A, Tiab D. Inferring Interwell Connectivity from Well Bottomhole-Pressure Fluctuations in Waterfloods. SPE Reservoir Evaluation & Engineering. 2008; 11(05): 874–881. doi: 10.2118/106881-PA

[9]Ji Y, Zhang L. A method for analyzing interwell connectivity based on gated recurrent network with knowledge interaction. Frontiers in Earth Science. 2025; 13: 1678611. doi: 10.3389/feart.2025.1678611

[10]Albertoni A, Lake LW. Inferring Interwell Connectivity Only From Well-Rate Fluctuations in Waterfloods. SPE Reservoir Evaluation & Engineering. 2003; 6(01): 6–16. doi: 10.2118/83381-PA

[11]Ye H, Deng J, Ma J, et al. Interwell Connectivity Analysis Method Based on Injection–Production Data Time and Space Scale Coupling. Processes. 2025; 13(2): 373. doi: 10.3390/pr13020373

[12]Yousef AA, Lake LW, Jensen JL. Analysis and Interpretation of Interwell Connectivity From Production and Injection Rate Fluctuations Using a Capacitance Model. In: Proceedings of the SPE/DOE Symposium on Improved Oil Recovery; 22 April 2006; Tulsa, OK, USA. doi: 10.2118/99998-MS

[13]Moreno GA, Lake LW. On the uncertainty of interwell connectivity estimations from the capacitance-resistance model. Petroleum Science. 2014; 11(2): 265–271. doi: 10.1007/s12182-014-0339-0

[14]Holanda RWD, Gildin E, Jensen JL, et al. A State-of-the-Art Literature Review on Capacitance Resistance Models for Reservoir Characterization and Performance Forecasting. Energies. 2018; 11(12): 3368. doi: 10.3390/en11123368

[15]Male F. Pywaterflood: Well connectivity analysis throughcapacitance-resistance modeling. Journal of Open Source Software. 2024; 9(95): 6191. doi: 10.21105/joss.06191

[16]Yuan G, Shangguan Y, Gao C, et al. Surfactant/Polymer Flooding Combined with Gel and Polymer Microsphere Plugging for Enhanced Oil Recovery in a Low-Permeability Reservoir. ACS Omega. 2026; 11(12): 19356–19368. doi: 10.1021/acsomega.5c12733

[17]Mwangupili O, Pu C. Enhancing Oil Recovery in Vertical Heterogeneous Sandstone Reservoirs Using Low-Frequency Pulsating Water Injection. Processes. 2025; 13(2): 296. doi: 10.3390/pr13020296

[18]Zhao H, Kang Z, Zhang X, et al. A Physics-Based Data-Driven Numerical Model for Reservoir History Matching and Prediction With a Field Application. SPE Journal. 2016; 21(06): 2175–2194. doi: 10.2118/173213-PA

[19]Guo Z, Reynolds AC, Zhao H. A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Waterflooding Performance. SPE Journal. 2018; 23(02): 367–395. doi: 10.2118/182660-PA

[20]Guo Z, Reynolds AC. INSIM-FT in three-dimensions with gravity. Journal of Computational Physics. 2019; 380: 143–169. doi: 10.1016/j.jcp.2018.12.016

[21]Zheng Z, Chen S, An F, et al. A GIN-Based Pre-Identification Method for Dominant Flow Channels in Connection-Element Reservoirs: An Optimized Ant Colony Algorithm Search Scheme. Processes. 2026; 14(10): 1605. doi: 10.3390/pr14101605

[22]Pope GA, Nelson RC. A Chemical Flooding Compositional Simulator. Society of Petroleum Engineers Journal. 1978; 18(05): 339–354. doi: 10.2118/6725-PA

[23]Delshad M, Pope GA, Sepehrnoori K. A compositional simulator for modeling surfactant enhanced aquifer remediation, 1 formulation. Journal of Contaminant Hydrology, 23(4): 303-327. doi: 10.1016/0169-7722(95)00106-9

[24]Thiele MR, Batycky RP, Blunt MJ. A Streamline-Based 3D Field-Scale Compositional Reservoir Simulator. In: Proceedings of the SPE Annual Technical Conference and Exhibition; 5 October 1997; San Antonio, TX, USA. doi: 10.2118/38889-MS

[25]Datta-Gupta A, King MJ. Streamline Simulation: Theory and Practice. Society of Petroleum Engineers; 2007. doi: 10.2118/9781555631116

[26]Sayarpour M, Zuluaga E, Kabir CS, et al. The use of capacitance–resistance models for rapid estimation of waterflood performance and optimization. Journal of Petroleum Science and Engineering. 2009; 69(3–4): 227–238. doi: 10.1016/j.petrol.2009.09.006

[27]Singh KG, Pathania T. Development and real field application of meshless generalized finite difference method for unconfined groundwater flow modelling. Mathematics and Computers in Simulation. 2026; 240: 332–346. doi: 10.1016/j.matcom.2025.07.027

[28]Tang Z, Pan H, Fu Z, et al. A least-squares generalized finite difference method for solving nonlinear reaction–diffusion systems. Engineering Analysis with Boundary Elements. 2025; 179: 106351. doi: 10.1016/j.enganabound.2025.106351

[29]Rao X, He X, Du K, et al. A Novel Projection-based Embedded Discrete Fracture Model (pEDFM) for Anisotropic Two-phase Flow Simulation Using Hybrid of Two-point Flux Approximation and Mimetic Finite Difference (TPFA-MFD) Methods. Journal of Computational Physics. 2024; 499: 112736. doi: 10.1016/j.jcp.2023.112736

[30]Zhao H, Zhan W, Yuhui Z, et al. A connection element method: Both a new computational method and a physical data-driven framework—Take subsurface two-phase flow as an example. Engineering Analysis with Boundary Elements. 2023; 151: 473–489. doi: 10.1016/j.enganabound.2023.03.021

[31]Tarjan R. Depth-First Search and Linear Graph Algorithms. SIAM Journal on Computing. 1972; 1(2): 146–160. doi: 10.1137/0201010

[32]Zhou Y, Wang L, Shi M, et al. Dynamic connectivity analysis of fracture-vuggy reservoir based on meshless method. Geoenergy Science and Engineering. 2025; 246: 213602. doi: 10.1016/j.geoen.2024.213602