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Abstract

Summary

With the arrival of Machine Learning (ML) techniques as effective alternatives to many legacy modeling steps, classical static and dynamic reservoir modeling workflows need re-adjustment. In particular, we will focus on Big Loop (BL) approaches to reservoir modeling, where subsurface disciplines create an integrated representation of the subsurface, calibrated to static and dynamic information, for reliable field development and reservoir management decision making. The commonality is

Finally, we show some of the specific ingredients of an evergreen ML-driven Big Loop workflow.

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/content/papers/10.3997/2214-4609.201902010
2019-06-03
2020-07-07
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