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Abstract

Summary

In many reservoir-modeling applications, geological uncertainty is treated by considering multiple realizations generated from a specified geological scenario. In reality, however, the geological scenario itself is uncertain, and the use of qualitative criteria for its specification may lead to inaccuracy in flow predictions. In this work, we introduce a systematic procedure for the determination of the most likely a posteriori geological scenario. As is common in geomodeling applications, the scenario is defined in terms of a training image (TI). We introduce continuous parameterizations for uncertain TI attributes such as channel thickness and orientation, and then determine optimal values for these and other key model quantities. Optimality is defined here in terms of the level of agreement between observed data and flow results for realizations generated from a given geological scenario. The optimum scenario is determined using Particle Swarm Optimization. In the second step of the methodology, a set of specific realizations, which provide closer agreement with observed data, is identified using a rejection-sampling method. The workflow is applied to a synthetic channelized system, and the procedure is repeated using several different ‘true’ reservoir realizations to gauge its performance with data from realizations that are more or less representative of the true scenario. Values for TI parameters found by our procedure are shown to be in reasonable agreement with those of the true scenario in all cases considered. In addition, following the identification of specific realizations using rejection sampling, predicted flow results are shown to be of similar quality to those from the true scenario.

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/content/papers/10.3997/2214-4609.20141780
2014-09-08
2024-04-20

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Optimization-based Framework for Geological Scenario Determination Using Parameterized Training Images
Conference Proceedings 2014, 1 (2014); https://doi.org/10.3997/2214-4609.20141780
/content/papers/10.3997/2214-4609.20141780
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