Reducing uncertainty in conceptual prior models of complex geologic systems via integration of flow response data

DOI10.1007/s10596-019-09908-6zbMath1434.86033OpenAlexW2999701650WikidataQ126398309 ScholiaQ126398309MaRDI QIDQ2187898

Azarang Golmohammadi, Behnam Jafarpour

Publication date: 3 June 2020

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-019-09908-6

zbMATH Keywords

inverse problemmodel calibrationpattern learning\(k\) nearest neighbor (\(k\)-NN)complex geologic faciesgeologic scenario selectionsubsurface flow modeling

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Quadratic programming (90C20) Inverse problems in geophysics (86A22)

Uses Software

• EnKF

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• A probability conditioning method (PCM) for nonlinear flow data integration into multipoint statistical facies simulation
• The probability perturbation method: a new look at Bayesian inverse modeling
• Simultaneous estimation of geologic and reservoir state variables within an ensemble-based multiple-point statistic framework
• Conditioning of multiple-point statistics facies simulations to tomographic images
• A distance-based prior model parameterization for constraining solutions of spatial inverse problems
• Prior model identification during subsurface flow data integration with adaptive sparse representation techniques
• Hybrid inversion method to estimate hydraulic transmissivity by combining multiple-point statistics and a direct inversion method
• Exploiting sparsity in solving PDE-constrained inverse problems: application to subsurface flow model calibration
• Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
• Block-Sparse Signals: Uncertainty Relations and Efficient Recovery

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