1887

Abstract

Summary

Quantifying uncertainty on reserves estimation requires considering structural, geological and dynamic uncertainties. We address the problem of uncertainty characterization of a channelized reservoir model and we discuss an approach to propagate both static and dynamic uncertainties for estimating reserves distribution integrating all available static data.

The approach is based on a kriging response surface method where specific parameterizations and parameter transformations are proposed to sample structural uncertainty while combining sampling of static and dynamic uncertainties such as architectural elements limits, channels geometry and proportions, rocktypes and associated petrophysical properties, fault transmissibility, vertical anisotropy, permeability. The method is applied to a real green field characterized by complex multi-level heterogeneities. Even for this very complex and high dimensional problem we obtain a good approximation of the reference reserves and in place distributions obtained from a full Monte Carlo method: the response surface can correctly propagate all the most relevant uncertainties, using only a reasonable number of model runs (typically hundreds).

We show that in the considered case study, the proposed approach outperforms our in-house workflow to estimate reserves distribution based on proprietary software. The method should allow reservoir engineers to perform a more efficient reserves distribution evaluation and also dynamic data integration at least when the small scale heterogeneities have a limited impact on the reserves.

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/content/papers/10.3997/2214-4609.20141903
2014-09-08
2024-03-29
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References

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