Ensemble-based assimilation of nonlinearly related dynamic data in reservoir models exhibiting non-Gaussian characteristics

DOI10.1007/S11004-018-9762-XzbMath1406.86022OpenAlexW2891503941MaRDI QIDQ1715419

Devesh Kumar, Sanjay Srinivasan

Publication date: 4 February 2019

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

Full work available at URL: https://doi.org/10.1007/s11004-018-9762-x

zbMATH Keywords

ensemblenon-Gaussian random fielddynamic data assimilationindicator transform methodsnon-linear flow models

Mathematics Subject Classification ID

Inverse problems in geophysics (86A22)

Related Items (6)

Resource model updating for compositional geometallurgical variables ⋮ A rapid updating method to predict grade heterogeneity at smaller scales ⋮ Resource and grade control model updating for underground mining production settings ⋮ Ensemble-based seismic and production data assimilation using selection Kalman model ⋮ Contaminant spill in a sandbox with non-Gaussian conductivities: simultaneous identification by the restart normal-score ensemble Kalman filter ⋮ A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping

Uses Software

• Unnamed Item

Cites Work

• Unnamed Item
• Conditional simulation of complex geological structures using multiple-point statistics
• Optimization with the gradual deformation method
• History Matching Under Geological Control: Application to a North Sea Reservoir
• The ensemble Kalman filter for combined state and parameter estimation

This page was built for publication: Ensemble-based assimilation of nonlinearly related dynamic data in reservoir models exhibiting non-Gaussian characteristics