Enhanced History Matching Process by Incorporation of Saturation Logs as Model Selection Criteria

Nowadays, thanks to the digital transformation of the oil and gas industry, there is a more diverse range of data available from production wells to history match reservoir simulation models. Still, conventional history matching methods continue using a limited set of observed data as validation parameters or model selection criteria. Therefore, sometimes critical factors controlling the fluid dynamic, and consequently the production forecasting are overlooked.
One of the challenges found in almost any history matching process is the precise estimation of fluid saturation and waterfront movement during the simulation period, however, despite being demonstrated that using surveillance logs into reservoir characterisation improves the model representation, not much effort has been allocated to incorporate saturation well logs into the core of the history matching workflows.
Therefore, this paper proposes a methodology for an alternative history matching process enhanced by the incorporation of a simplified binary interpretation of reservoir saturation logs (RST) as objective function. Incorporating fluids saturation logs during the history matching phase unlocks the possibility to adjust or select models that better represent the near wellbore waterfront movement, which is particularly important for uncertainty mitigation during future well intervention assessments in water driven reservoirs.
For the purposes of this study, a semi-synthetic open-source reservoir model was used as base case to evaluate the proposed methodology. The reservoir model represents a water driven, highly heterogenous sandstone reservoir from Namorado field in Brazil.
To effectively compare the proposed methodology against the conventional methods, a commercial reservoir simulator was used in combination with a state-of-the-art benchmarking workflow based on the Big LoopTM approach.
A well-known group of binary metrics were evaluated to be used as the objective function, and the Matthew correlation coefficient (MCC) has been proved to offer the best results when using binary data from water saturation logs. History matching results obtained with the proposed methodology allowed the selection of a more reliable group of reservoir models especially for cases with high heterogeneity. The methodology also offers additional information and understanding of sweep behaviour behind the well casing at specific production zones, thus revealing full model potential to define new wells and reservoir development opportunities. The methodology proposed in this paper does not compromise conventional methods used to evaluate the history matching quality, instead, it should be considered as an option for multi-parameter history matching workflows. The contribution to knowledge from this research is the methodology, which has also been applied in a real case for the model build and validation of a North Sea turbidite reservoir, with similar enhancements in history matching as presented in this publication.