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- W4378231359 abstract "Summary Outlier Identification and Log Reconstruction Using Deep Learning When analyzing wellbore logs, one of the main objectives of the petrophysicist is to derive the formation properties for the region near the wellbore. The environmental and technical challenges faced during wellbore log acquisition and log processing might cause the wellbore logs to be partially or entirely compromised, with part of the measurements not correctly reflecting the formation properties. Identifying the intervals with one or more logs associated with anomalous data, designated as outliers, and suggesting more likely values for those logs are challenging even for experts. Our work provides a solution to create a robust per-log outlier identification and provide higher-quality data estimated for the zones where the flag was activated using deep learning on multivariate series. The scope of this work includes the basic wellbore logs: gamma-ray, density, neutron porosity, and acoustic slowness, and it provides for the possibility of having one or more corrupted or missing log data. In this work, we explicit the advantages over traditional solutions, describe the self-supervised methodology adopted to train the deep learning method and how to define the outliers, and present two applications and field results on test wells." @default.
- W4378231359 created "2023-05-26" @default.
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- W4378231359 date "2023-01-01" @default.
- W4378231359 modified "2023-09-30" @default.
- W4378231359 title "Outlier Identification and Log Reconstruction Using Deep Learning" @default.
- W4378231359 doi "https://doi.org/10.3997/2214-4609.202310792" @default.
- W4378231359 hasPublicationYear "2023" @default.
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