Fast diagnosis of reservoir simulation models based on 4D seismic similarity indicators

In the last ten years, 4D seismic (4DS) data acquisition is evolving to permanent reservoir monitoring (PRM) systems where sensors are installed at the ocean-bottom, collecting seismic data according to the project's monitoring demand. Simultaneously, reservoir management workflows evolved to include uncertainties, where multiple reservoir models may be considered. Model-based approaches for developing and managing a field rely on error minimizations between modelled and measured data, traditionally from well production data, and eventually adapted to 4DS data. This study presents a fast, robust and unsupervised workflow to provide a comprehensive diagnosis of multiple reservoir simulation models using similarity indicators with observed 4DS. The methodology comprises a seismic forward modelling to convert hundreds of models from the reservoir engineering domain to the seismic domain. The diagnosis includes a novel region-by-region approach to compare the predicted synthetic seismic response with the observed 4DS anomalies using ternary maps generated from Gaussian mixture models (GMM), in addition to a magnitude metric. The methodology is tested in an ultra-deep turbidite field from the Campos basin in Brazil with a PRM system that captured various 4DS anomalies of different polarity, magnitude, shapes and sizes over several production and injection years. The contributions of this work are demonstrated in four applications: (1) feedback on various iterations of geomodelling, (2) feedback on well and seismic data assimilation, (3) quick evaluation of a new seismic monitor, and (4) ranking models for further decision-making studies. The workflow advantages are proved along the model-based reservoir management outline. For application (1), we successfully flagged which 4DS anomalies were being honored in the simulation models and quantified the impact of introducing features interpreted from seismic monitors in the geomodelling. For application (2), we quantified simulation model improvements provided by data assimilation. For application (3), we rapidly evaluated the quality of the seismic monitor against the existing simulation models as soon as the new seismic acquisition and processing was complete, validating the requirement to re-visit the simulation models. Finally, the workflow was crucial to select best models, out of hundreds, for the decision-making process, in application (4).