By Topic

Application of artificial neural networks in lithofacies interpretation used for 3D geological modelling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xueping Ma ; Coll. of Marine Geosci., Ocean Univ. of China, Qingdao, China ; Jinliang Zhang ; Hongjuan Zhao

This paper represents a study using Artificial Neural Networks (ANN) to perform automatic interpretation of lithofacies in a reservoir scale. This technique having been used successfully to interpret lithofacies automatically in the Sha20 Block, Shanian oilfield. Description and interpretation from a cored section in the key well was used to train the Supervised neural network. Having trained the network, it was then used to recognise and interpret the units vertically and laterally in the studied reservoir. The unsupervised neural network was run to classify the cored interval into 2 and 6 classes respectively and the results were then compared with the supervised network output. The results were observed to be over 87% accurate. Then a 3D geological model was built using the sequential indicator simulation method, the excellent results obtained from the developed model shows that the method is quite effective and gets satisfying prediction precision for the lithofacies in reservoir modeling.

Published in:

Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on  (Volume:4 )

Date of Conference:

8-9 Aug. 2009