Ensemble-based history matching of the Edvard Grieg field using 4D seismic data

Abstract

The Edvard Grieg field is a highly complex and heterogeneous reservoir with an extensive fault structure and a mixture of sandstone, conglomerate, and shale. In this paper, we present a complete workflow for history matching the Edvard Grieg field using an ensemble smoother for Bayesian inference. An important aspect of the workflow is a methodology to check that the prior assumptions are suitable for assimilating the data, and procedures to verify that the posterior results are plausible and credible. We thoroughly describe several tools and visualization techniques for these purposes. Using these methods we show how to identify important parameters of the model. Furthermore, we utilize new compression methods for better handling large datasets. Simulating fluid flow and seismic response for reservoirs of this size and complexity requires high numerical resolution and accurate seismic models. We present a novel dual-model concept for a better representation of seismic data and attributes, that deploy different models for the underground depending on simulated properties. Results from history matching show that we can improve data matches for both production data and different seismic attributes. Updated parameters give new insight into the reservoir dynamics, and are calibrated to better represent water movement and pressure.