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Tools for model-reduction and accelerated reservoir simulation

To optimize hydrocarbon reservoir recovery, understanding and predicting flow and transport processes is crucial. Geo-cellular models, representing complex rock formations, often contain millions of cells, requiring hours for simulation. To expedite workflows, coarsening the grid and upscaling petrophysical parameters is necessary, reducing model sizes. However, this introduces approximation errors, especially in high-contrast media models. Balancing simulation speed and accuracy remains a critical technical challenge in enhancing reservoir recovery.

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Example of hierarchical coarsening used to generate reduced, multi-fidelity models that preserve geological structures. The model have higher resolution in zones expected to have high flow and low resolution in the far-field regions.

Through a continuous series of collaborative projects, stretching more than a decade, we have developed model reduction tools.  Specifically, we have developed an innovative framework for creating reduced-order reservoir models that combines agglomeration of cells from existing high-fidelity reservoir models and flow-based upscaling. Employing a hierarchical grid-coarsening approach, the framework ensures accurate preservation of geological structures. By leveraging flow data to identify regions of varying flow, users can adapt model resolution differently across the reservoir. The framework offers diverse coarsening strategies, allowing users to tailor reduced models to critical geological aspects. By selectively preserving key features and aggressively coarsening others, users can closely align reduced models with high-fidelity counterparts. Simple flow diagnostics predict the accuracy of resulting reduced models, utilizing time-of-flight and volumetric well communication.

The new methods are implemented in the Client's inhouse workflow tools and have been used with success on several assets. Rudimentary versions are also available in the module for adapted coarsening in MRST

Key Factors

Project duration

01/01/2009 - 31/12/2022

Project type

Industry project


ExxonMobil Upstream Research Company


  • A. Guion, B. Skaflestad, K.-A. Lie, and X.-H. Wu. Validation of a non-uniform coarsening and upscaling framework. SPE Reservoir Simulation Conference, Galveston, Texas, USA, 10-11 April, 2019. DOI: 10.2118/193891-MS
  • K.-A. Lie, K. Kedia, B. Skaflestad, X. Wang, Y. Yang, X.-H. Wu, and N. Hoda. A general non-uniform coarsening and upscaling framework for reduced-order modeling. SPE Reservoir Simulation Conference, Montgomery, Texas, USA, 20-22 February 2017. DOI: 10.2118/182681-MS
  • K.-A. Lie, J. R. Natvig, S. Krogstad, Y. Yang, and X.-H. Wu. Grid adaptation for the Dirichlet-Neumann representation method and the multiscale mixed finite-element method. Comput. Geosci., Vol. 18, No. 3, pp. 357-372, 2014. DOI: 10.1007/s10596-013-9397-4

Example uses reported by client:

  • S. K. Verma, Y. Xu, S. He, and A. Sanai. Stochastic optimization workflow for non-uniform coarsening of simulation grids and location and sequencing of wells. ADIPEC, Abu Dhabi, UAE, October 2022. DOI: 10.2118/211372-MS
  • A. Sanaei, S. He, J. Pope, S. Verma, R. Mifflin, A. El-Bakry. Apply reduced-physics modeling to accelerate depletion planning optimization under subsurface uncertainty. SPE Annual Technical Conference and Exhibition, Houston, Texas, USA, October 2022. DOI: 10.2118/210217-MS

Project team

  • Jostein Roald Natvig

    Jostein Roald Natvig

    Senior Scientific Computing Engineer SLB
  • Jørg E. Aarnes

    Jørg E. Aarnes

    Global lead - Hydrogen and CCS DnV
  • Stein Krogstad

    Stein Krogstad

    Senior Research Scientist