Coupling level set methods with the ensemble kalman filter for conditioning geological facies models to well and production data

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Descripción: In the work we developed a new methodology based on the ensemble Kalman filter (EnKF) and the level set method for the continuous model updating of geological facies with respect to production and static (well logs) data. We modelled geological facies using a level set representation and further conditioned them to production and static data using the ensemble Kalman filter. The history matching was done in a continuous fashion since the filter does not depend on previous states and updates parameters and states of the physical system as it receives data from the fields. The methodology is completely new and may provide an alternative to the pluri-Gaussian method with competitive complexity times. Further, the methodology allows to involve prior knowledge of the reservoir as a template base case in a Bayesian-like update. The methodology was tested and compare to others and implemented on a real 3D north-sea and synthetic reservoirs.
Citación recomendada (normas APA)
: David Leonardo Moreno Bedoya, "Coupling level set methods with the ensemble kalman filter for conditioning geological facies models to well and production data", -:-, 2009. Consultado en línea en la Biblioteca Digital de Bogotá (https://www.bibliotecadigitaldebogota.gov.co/resources/2081273/), el día 2025-05-16.

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