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...
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| Vydáno v: | Geophysical Prospecting Ročník 69; číslo 6; s. 1295 - 1315 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Houten
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01.07.2021
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| ISSN: | 0016-8025, 1365-2478 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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. |
| Author | Liu, Xingye Li, Jingye Li, Bin Li, Qingchun Chen, Yangkang Chen, Xiaohong |
| Author_xml | – sequence: 1 givenname: Xingye orcidid: 0000-0002-9193-1075 surname: Liu fullname: Liu, Xingye email: lwxwyh506673@126.com organization: Xi'an University of Science and Technology – sequence: 2 givenname: Bin surname: Li fullname: Li, Bin organization: Tongji University – sequence: 3 givenname: Jingye surname: Li fullname: Li, Jingye organization: China University of Petroleum‐Beijing – sequence: 4 givenname: Xiaohong surname: Chen fullname: Chen, Xiaohong organization: China University of Petroleum‐Beijing – sequence: 5 givenname: Qingchun surname: Li fullname: Li, Qingchun organization: Chang'an University – sequence: 6 givenname: Yangkang surname: Chen fullname: Chen, Yangkang organization: Zhejiang University |
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Facies boundaries are critical for flow performance in a reservoir and are significant for lithofacies identification in well interpretation and... Facies boundaries are critical for flow performance in a reservoir and are significant for lithofacies identification in well interpretation and reservoir... |
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| SubjectTerms | Accuracy Classification Feasibility studies Identification Lithofacies Machine learning Parameter estimation Regularization Reservoir geophysics Reservoirs Seismic data Seismic surveys Seismics Training |
| Title | Semi‐supervised deep autoencoder for seismic facies classification |
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