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Big Loop Reservoir Forecasting in the Machine Learning eraNormal access

Authors: F.O. Alpak and M. Araya-Polo
Event name: 81st EAGE Conference and Exhibition 2019 Workshop Programme
Session: WS17 Live 3D Geological Models, from Core to Simulation and Real-time Field Economics
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201902010
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 343.01Kb )
Price: € 20

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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