Zobrazeno 1 - 10
of 1 891
pro vyhledávání: '"A. T. Colwell"'
Autor:
Gene T. Colwell
Publikováno v:
Proceeding of Heat Pipe Technology: Volume 1. Fundamentals and Experimental Studies.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fdc6a704baa14d0eb208da2ca3db40b9
https://doi.org/10.1615/ihpc1990v1.500
https://doi.org/10.1615/ihpc1990v1.500
Autor:
T. Colwell
Publikováno v:
Second EAGE Subsurface Intelligence Workshop.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::991a48e2d8089e29e30d603e805ef719
https://doi.org/10.3997/2214-4609.2022616027
https://doi.org/10.3997/2214-4609.2022616027
Publikováno v:
Digital Subsurface Conference in Latin America.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e12e426295b039274f882b781f3d26be
https://doi.org/10.3997/2214-4609.202181014
https://doi.org/10.3997/2214-4609.202181014
Publikováno v:
First EAGE Conference on Seismic Inversion.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e78d42ec36c1b74e5428188152c02d7
https://doi.org/10.3997/2214-4609.202037018
https://doi.org/10.3997/2214-4609.202037018
Publikováno v:
Fifth EAGE Workshop on Rock Physics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::72ae232f1000eca29c750cfef9a394a2
https://doi.org/10.3997/2214-4609.2020603029
https://doi.org/10.3997/2214-4609.2020603029
Publikováno v:
82nd EAGE Annual Conference & Exhibition.
Summary Deep Neural Networks (DNNs) are used to predict reservoir properties from well and seismic data. Log properties from the well data serve as the targets while the seismic data serve as the input features. These DNNs require big data. In the re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d7b488cd6ff46ae68adce4f416ea70c
https://doi.org/10.3997/2214-4609.202011797
https://doi.org/10.3997/2214-4609.202011797
Publikováno v:
First EAGE Conference on Machine Learning in Americas.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e31aa1a36206506ee7bccd2ff93837ff
https://doi.org/10.3997/2214-4609.202084013
https://doi.org/10.3997/2214-4609.202084013
Publikováno v:
EAGE Subsurface Intelligence Workshop.
Summary The challenge in adopting neural networks in the geosciences is the relative scarcity of labeled training data. This presentation demonstrates an approach to augment the amount of data used to train the neural network. Rock Physics theory is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a473d8b0294234abf29f38e3f6f0ec3a
https://doi.org/10.3997/2214-4609.2019x610102
https://doi.org/10.3997/2214-4609.2019x610102
Autor:
T. Colwell, Ø. Kjøsnes
Publikováno v:
Proceedings.
Machine learning has been gaining momentum thanks to a new powerful technique called deep learning (Bengio, 2016). These improvements are due to increasing the depth of neural networks to more than one hidden layer. This study uses a Deep Feed-forwar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8695d72a30d46d095a3f0d6cd94f6d48
https://doi.org/10.3997/2214-4609.201803009
https://doi.org/10.3997/2214-4609.201803009