Uncertainty quantification in history matching of fluvial reservoirs with connectivity analysis and realistic geology

One of the challenges in automatic history matching is to ensure the preservation of the realistic geological features of reservoir models. Realism of the history matched model is vital because of its impact on the flow response and the reliability of the model predictions. The issue is that models with unrealistic geological features can still match history but would fail to deliver reliable predictions. The work tackles the following problems in history matching: (1) identify only realistic geometry of geological bodies in history matching using non-uniform intelligent prior distributions for the uncertain geological parameters; (2) assess uncertainty in the connectivity between permeable geobodies and automatically select the geomodel realisations best matching the production data. The intelligent geological priors and the connectivity measures are incorporated in an automated history matching framework, where adaptive stochastic sampling is used to find multiple history matched models to quantify uncertainty of the reservoir predictions. Use of intelligent priors, also, improves the history matching efficiency by restricting the search only to the geologically realistic models. Connectivity control improves flexibly of the model definition and allows handling uncertainty from multiple stochastic realisations in a more realistic way.