Work Package three: Reservoir uncertainty analysis

Key team members:

Team lead: Professor Klaus Mosegaard, The Niels Bohr Institure, University of Copenhagen. 

A Post Doc and a PhD student will be recruited to solve the requested scientific tasks while interacting closely with the other WPs.

Overview

Uncertainty analysis of reservoir models from geological, geophysical and production data is currently based on summary evaluations and physically inconsistent error propagation methods. Statistical predictions about reservoirs are mostly based on: 

  1. Large geological data bases, or 
  2. Subjective expert assessments of geophysical and geological data. 

These limitations may have significant consequences for the appraisal of the reservoir, and a miscalculation of these factors may seriously reduce the quality of production scenario evaluations.

The aim of this WP is to develop an uncertainty evaluation system based on a consistent, probabilistic approach to geophysical / geostatistical inversion and flow data analysis. Monte Carlo methods for uncertainty quantification in large-scale simulations will be explored and possibly coupled with the methods for optimisation and control (WP4).