Abstract

Model validation using available history data demands that the model reflects relevant behaviour occurring in the subsurface. Reservoir models lacking in proper modelling of essential reservoir behaviour are poor representatives of reality. Usually, calibrating these models to history data require excessive use of parameter fudging to achieve meaningful match results. To combat this issue, an efficient approach to improve reservoir understanding is presented that investigates hidden reservoir behaviour at field, region and well level.

The model screening approach uses static grid properties to explore unmatchable models for hidden reservoir behaviour. Permeability and/or porosity is defined as an input for the minimization problem with preset uncertainty limits ranging from zero to values greater than the default maximum values. The adjoint-state method is employed to condition simulation models to observation data. Unrealistic match results are realized at this stage which cannot be used for performance forecasting. Logical interpretation are generated for the extreme property updates. Feedback from interpretations are then used to enhance reservoir understanding. The PUNQ-S3 model is used to test proposed screening methodology.

A variant of the adjoint-state method executed in an unrestrained manner proved its capability to reveal hidden reservoir behaviour. A handful of patterns that draws attention to hidden reservoir behaviour were observed in permeability and/or porosity distribution. In the case of a hidden fault, the permeability distribution around influenced wells experiences a sharp contrasting change; the high permeability region seeks more oil while the low permeability region constrains oil production. Also, in the case of a hidden aquifer, we observed an extreme vertical porosity and permeability update travelling down through all layers into the oil-water contact zone. Hidden channels are represented as high permeability streaks connecting affected wells. So far, the model screening approach suffers from one limitation – an inability to differentiate between a reservoir boundary and a fault close to the boundary.

To guarantee that geological consistency is not sacrificed at the expense of history matching, local or global updates made to the starting model are restricted to true model variogram. The most crucial finding is that the production history must account for the physical mechanisms occurring in the representative reservoir. On the contrary, noisy production data or errors in field measurements limit the effectiveness of the adjoint-state method to provide insights on hidden reservoir behaviour.

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