Uncertainty quantification in reservoir prediction. I: Model realism in history matching using geological prior definitions

DOI10.1007/s11004-018-9774-6zbMath1411.86006OpenAlexW2901251300WikidataQ128888164 ScholiaQ128888164MaRDI QIDQ1740335

Dan Arnold, Temistocles Rojas, Mike Christie, Vasily Demyanov

Publication date: 30 April 2019

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

Full work available at URL: https://doi.org/10.1007/s11004-018-9774-6

zbMATH Keywords

classificationuncertaintyinverse problemsgeostatisticsmodel calibrationprior knowledgesupport vectorreservoir modellingfluvial geologynatural analogues

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) Inverse problems in geophysics (86A22) Geostatistics (86A32)

Related Items (2)

Uncertainty quantification in game theory ⋮ Uncertainty quantification in reservoir prediction. II: Handling uncertainty in the geological scenario

Uses Software

• SGeMS

Cites Work

• Unnamed Item
• History matching through a smooth formulation of multiple-point statistics
• Representing spatial uncertainty using distances and kernels
• Reconstruction of channelized systems through a conditioned reverse migration method
• Uncertainty quantification in reservoir prediction. II: Handling uncertainty in the geological scenario
• Production forecasting and uncertainty quantification for naturally fractured reservoirs using a new data-space inversion procedure
• Conditional simulation of complex geological structures using multiple-point statistics
• Uncertainty quantification for porous media flows
• Error models for reducing history match bias
• Estimating the Support of a High-Dimensional Distribution
• Machine Learning for Spatial Environmental Data

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