Automated reservoir modeling using deep generative networks
AssigneeExxonMobil Upstream Research Company
InventorsHuseyin Denli, Cody J. MacDonald, Victoria M. Som De Cerff
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AssigneeExxonMobil Upstream Research Company
InventorsHuseyin Denli, Cody J. MacDonald, Victoria M. Som De Cerff
Estimating the term…
A method for generating one or more reservoir models using machine learning is provided. Generating reservoir models is typically a time-intensive idiosyncratic process. However, machine learning may be used to generate one or more reservoir models that characterize the subsurface. The machine learning may use geological data, geological concepts, reservoir stratigraphic configurations, and one or more input geological models in order to generate the one or more reservoir models. As one example, a generative adversarial network (GAN) may be used as the machine learning methodology. The GAN includes two neural networks, including a generative network (which generates candidate reservoir models) and a discriminative network (which evaluates the candidate reservoir models), contest with each other in order to generate the reservoir models.
A machine learning method for generating one or more geological models of a subsurface, the method comprising:
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