Advances in Reservoir Simulation

This synthesis highlights innovations addressing reservoir heterogeneity and fracture dynamics through integrated numerical modeling, data assimilation, and multi-physics coupling. Ensemble-based algorithms (e.g., ES-MDA) enhance history matching by assimilating 4D seismic and production data, reduc...

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Format:	Online
Idioma:	anglès
Publicat:	MDPI - Multidisciplinary Digital Publishing Institute 2025
Matèries:	joint history matching iterative ensemble smoother production, tracer and 4D seismic data virtual flow measurement multiphase flow reservoir fluid simulator numerical methods hydro-mechanical fracture propagation production fully coupled stress interference proxy modeling ES-MDA history matching reservoir optimization naturally fractured reservoirs poro-elastic environment finite element technique permeability porosity complex carbonate reservoirs rock typing petrophysical correlations formation creep downhole temperature change finite element method integrity of cement sheath operating pressure elastic modulus of cement sheath fractured well proxy model physics-informed neural network deep learning machine learning four-dimensional seismic ensemble smoother with multiple data assimilation distance-to-front streamlines clayey silt hydrate perforated completion temporary plugging fracturing stimulated rock area low-permeability reservoir induced fracture elliptic-flow composite well-test model thema EDItEUR::P Mathematics and Science::PS Biology, life sciences
Accés en línia:	ONIX_20250812T110751_9783725838165_195
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Sumari:	This synthesis highlights innovations addressing reservoir heterogeneity and fracture dynamics through integrated numerical modeling, data assimilation, and multi-physics coupling. Ensemble-based algorithms (e.g., ES-MDA) enhance history matching by assimilating 4D seismic and production data, reducing uncertainties by 15–20%. Hydro-mechanical models optimized with true triaxial experiments guide Discrete Fracture Network (DFN)-driven hydraulic fracturing, boosting shale gas productivity by 40%. Proxy models like INSIM-FT and Physics-Informed Neural Networks (PINNs) enable rapid simulation, cutting computational time from weeks to hours while maintaining >85% accuracy. Machine learning (XGBoost) achieves 92% permeability prediction in carbonates, while dynamic heterogeneity analysis reveals fracture-induced permeability contrasts exceeding 103. Geomechanical frameworks quantify risks in salt cavern storage (0.12% annual creep strain) and fractured reservoirs, extending operational lifespans by 20%. Field applications demonstrate 8% recovery gains in carbonate fields via 4D seismic integration and 60% leakage risk reduction through multi-physics cement design. Emerging trends fuse data-physics models (30–50% efficiency gains) and cross-scale simulations, while challenges persist in proppant transport modeling and sparse 4D data. Future directions prioritize quantum computing for fracture networks, IoT-enabled digital twins, and adapting reservoir engineering to carbon sequestration, positioning the field as pivotal for sustainable energy transition.

Advances in Reservoir Simulation

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