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dc.contributor.authorWang, Chao
dc.contributor.authorLiu, Gonghui
dc.contributor.authorYang, Zhirong
dc.contributor.authorLi, Jun
dc.contributor.authorZhang, Tao
dc.contributor.authorJiang, Hailong
dc.contributor.authorCao, Chenguang
dc.date.accessioned2021-03-29T08:18:10Z
dc.date.available2021-03-29T08:18:10Z
dc.date.created2020-08-27T15:07:33Z
dc.date.issued2020
dc.identifier.issn1875-5100
dc.identifier.urihttps://hdl.handle.net/11250/2735852
dc.description.abstractDrilling complications, which are usually hard to be discovered in time using the traditional surface detecting methods, result in much time and money wasted in handling these problems. Restricted to data transmission speed with the measurement while drilling (MWD), downhole measured data is usually ignored in downhole complications detection. And the surface detection methods with some pressure and rate of flow sensors always demand much professional knowledge and contain detection delay. In this paper, we used the measured downhole parameters to discover the drilling complications combined with deep leaning methods. Firstly, we described the difficulties of applying deep learning methods into the exploring drilling data. Then we used wavelet decomposition and reconstruction method to reduce the influence of the data trend with well depth and remove the high frequency noise. The fluctuation items coupling analysis method, consisted with rock breaking theory and transient fluctuating pressure theory, was established to make sure whether the wavelet reconstruction results contain the information to do detection. We applied a deep learning method called Bidirectional Generative Adversarial Network (BiGAN) in complications detection. BiGAN can distinguish whether the data belongs to normal working condition data or not. An end to end deep neural network mainly composed with one dimensional convolutional neural network was established to determine the specific kind of normal working condition. Then, large numbers of real field drilling data collected by the measuring tool were used to test the detection method. The testing results indicated that BiGAN indeed learned the normal working condition data distribution and the end to end network performed high accuracy in the normal working conditions classification. Therefore, we chose the combination of BiGAN and the supervised neural network to detect drilling complications with six field cases. The experiment results showed that the detection method can detect the complications much earlier than the surface detection results except for nozzle clogging case.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.titleDownhole working conditions analysis and drilling complications detection method based on deep learningen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume81en_US
dc.source.journalJournal of Natural Gas Science and Engineeringen_US
dc.identifier.doihttps://doi.org/10.1016/j.jngse.2020.103485
dc.identifier.cristin1825595
dc.description.localcodeThis article will not be available due to copyright restrictions (c) 2020 by Elsevieren_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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