This document is an expanded abstract.


This paper introduces the application to a real field case of an automatic iterative geostatistical history matching technique, integrating geological and engineering consistency. Current trends in the industry reflect a growing interest towards the development of workflows that simultaneously integrate petrophysical modeling with dynamic calibration of reservoir models to historical production data. Contrary to manual history matching techniques, where model perturbation often disregards geological or physical realism, the proposed history matching approach introduces geological consistency through geostatistical simulation and physical realism by using streamline regionalization while holding the predictive capability of resulting petrophysical models. In the proposed methodology, the reservoir static properties are iteratively updated by stochastic sequential simulation and co-simulation, constrained to production data, while streamline information is used for electing preponderant flow production regions of the model, as the focus for property perturbation. In order to capture the complex subsurface heterogeneities of the reservoir, petrophysical property realizations were obtained using the Direct Sequential Simulation and co-Simulation algorithm, with Multi-local Distribution Functions (Nunes et al, 2017). The location and proportion of reservoir facies is also automatically updated throughout the iterative procedure, using Bayesian Classification. The proposed approach was successfully applied to a real case study, located in North-East onshore Brazil, resulting in multiple history matched models that better reproduce historic data.


Traditional history matching (HM) workflows often rely on applying property multipliers, in a tradeoff between model realism and production matching, often resulting in geologically unreliable models and with debatable predictive capabilities. Recently, HM methodologies have been focusing on the integration of geostatistical simulations. These approaches aim to tackle the challenge of obtaining geologically realistic models, based on geological parameters of interest, without the need for data transformation or property multipliers.

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