Open AccessGenerative adversarial networks for modeling reservoirs with permeability anisotropy
Author(s) -
R Guliev
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1201/1/012066
Subject(s) - generative grammar , adversarial system , data assimilation , computer science , artificial intelligence , generative adversarial network , deep learning , permeability (electromagnetism) , machine learning , data mining , geology , physics , membrane , biology , meteorology , genetics
The geological model is a main element in describing the characteristics of hydrocarbon reservoirs. These models are usually obtained using geostatistical modeling techniques. Recently, methods based on deep learning algorithms have begun to be applied as a generator of a geologic models. However, there are still problems with how to assimilate dynamic data to the model. The goal of this work was to develop a deep learning algorithm - generative adversarial network (GAN) and demonstrate the process of generating a synthetic geological model: • Without integrating permeability data into the model • With data assimilation of well permeability data into the model The authors also assessed the possibility of creating a pair of generative-adversarial network-ensemble smoother to improve the closed-loop reservoir management of oil field development.