Semi‐supervised deep autoencoder for seismic facies classification

ABSTRACT Facies boundaries are critical for flow performance in a reservoir and are significant for lithofacies identification in well interpretation and reservoir prediction. Facies identification based on supervised machine learning methods usually requires a large amount of labelled data, which a...

Full description

Saved in:
Bibliographic Details
Published in:Geophysical Prospecting Vol. 69; no. 6; pp. 1295 - 1315
Main Authors: Liu, Xingye, Li, Bin, Li, Jingye, Chen, Xiaohong, Li, Qingchun, Chen, Yangkang
Format: Journal Article
Language:English
Published: Houten Wiley Subscription Services, Inc 01.07.2021
Subjects:
Accuracy
Classification
Feasibility studies
Identification
Lithofacies
Machine learning
Parameter estimation
Regularization
Reservoir geophysics
Reservoirs
Seismic data
Seismic surveys
Seismics
Training
ISSN:0016-8025, 1365-2478
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:ABSTRACT Facies boundaries are critical for flow performance in a reservoir and are significant for lithofacies identification in well interpretation and reservoir prediction. Facies identification based on supervised machine learning methods usually requires a large amount of labelled data, which are sometimes difficult to obtain. Here, we introduce the deep autoencoder to learn the hidden features and conduct facies classification from elastic attributes. Both labelled and unlabelled data are involved in the training process. Then, we develop a semi‐supervised deep autoencoder by taking the mean of intra‐class and the whole population of facies into account to construct a classification regularization term, thereby improving the classification accuracy and reducing the uncertainty. The new method inherits the profits of deep autoencoder and absorbs the information provided by labelled data. The proposed method performs well and produces promising results when it is used to address problems of reservoir prediction and facies identification. The new method is evaluated on both well and seismic data and compared with the conventional deep autoencoder method, which demonstrates its feasibility and superiority with respect to classification accuracy.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0016-8025
1365-2478
DOI:10.1111/1365-2478.13106