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Abstract

Summary

We propose the use of a Deep Learning (DL) algorithm for the real-time inversion of electromagnetic measurements acquired during geosteering operations. Moreover, we show that when the DL algorithm is equipped with a properly designed two-step loss function without regularization, it is possible to recover an uncertainty quantification map by analyzing certain cross-plots. We illustrate these ideas with a synthetic example based on piecewise 1D earth models.

The resulting uncertainty quantification map could be used to design better measurement acquisition systems for geosteering operations.

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/content/papers/10.3997/2214-4609.2021624005
2021-11-02
2024-04-27
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References

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