INSIM-BHP: A physics-based data-driven reservoir model for history matching and forecasting with bottomhole pressure and production rate data under waterflooding

Li, Ying; Önür, Mustafa

doi:10.1016/j.jcp.2022.111714

Cited by 19 publications

(1 citation statement)

References 41 publications

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“…The family of interwell numerical simulation models (INSIM) focus on the dynamic behavior and interactions between multiple wells and use analytical or semi-analytical methods to evolve pressures and fluid compositions within each interwell connection [18][19][20][21][22]. A more general approach, referred to by different authors as StellNet/GPSNet/FlowNet, is to use standard finite-volume methods to represent the interwell connections and also allow direct fluid communication among different flow paths without going through the wells [23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Data-driven modelling with coarse-grid network models

Lie

Krogstad

2023

Comput Geosci

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We propose to use a conventional simulator, formulated on the topology of a coarse volumetric 3D grid, as a data-driven network model that seeks to reproduce observed and predict future well responses. The conceptual difference from standard history matching is that the tunable network parameters are calibrated freely without regard to the physical interpretation of their calibrated values. The simplest version uses a minimal rectilinear mesh covering the assumed map outline and base/top surface of the reservoir. The resulting CGNet models fit immediately in any standard simulator and are very fast to evaluate because of the low cell count. We show that surprisingly accurate network models can be developed using grids with a few tens or hundreds of cells. Compared with similar interwell network models (e.g., Ren et al., 2019, 10.2118/193855-MS), a typical CGNet model has fewer computational cells but a richer connection graph and more tunable parameters. In our experience, CGNet models therefore calibrate better and are simpler to set up to reflect known fluid contacts, etc. For cases with poor vertical connection or internal fluid contacts, it is advantageous if the model has several horizontal layers in the network topology. We also show that starting with a good ballpark estimate of the reservoir volume is a precursor to a good calibration.

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