Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313449118> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W4313449118 endingPage "D157" @default.
- W4313449118 startingPage "D147" @default.
- W4313449118 abstract "As a key technology to evaluate cement bonds in the cased hole, an advanced ultrasonic logging tool combines pulse-echo and pitch-catch measurements in which the latter one provides reflections from the cement-formation interface (called third-interface-echo [TIE]) to evaluate the bond condition and determine casing eccentering as well as cement velocity. However, the TIE would be weak and not easy to pick due to the eccentered tool and casing and it would overlap with the strong multiple reflections between the casing inner surface and the transducer-housing tool. We have developed a deep learning workflow to extract weak TIE from noisy data and to preserve its amplitude at the same time. First, we use synthetic waveforms from thousands of finite-difference simulations as initial training data sets to train a deep learning network, which is modified from a network in speech separation. Then, the trained model is used to predict the field data through an active-learning strategy. The improved network is further used to extract the weak TIEs, which are not easy to pick in the initial deep learning model. Finally, the TIE waves image is converted to a pseudovelocity image to obtain the minimum traveltime path by solving the eikonal equation. The shortest traveltime path is used as the TIE arrival time. In addition, a 3D visualization is used to display the borehole shape from the picked arrival time. The applications in synthetic data and data set from a calibration well illustrate a good performance of our workflow in which the weakest TIE extracted from the network can reach 50 dB compared to the maximum amplitude in the full waveform. The picked arrival times can be used to reconstruct a borehole shape." @default.
- W4313449118 created "2023-01-06" @default.
- W4313449118 creator A5030863883 @default.
- W4313449118 creator A5039065462 @default.
- W4313449118 creator A5053725057 @default.
- W4313449118 date "2023-03-01" @default.
- W4313449118 modified "2023-10-17" @default.
- W4313449118 title "A deep learning workflow for weak reflection extraction in pitch-catch measurements in the cased hole" @default.
- W4313449118 cites W1969609783 @default.
- W4313449118 cites W2016869034 @default.
- W4313449118 cites W2023254210 @default.
- W4313449118 cites W2086756598 @default.
- W4313449118 cites W2130744222 @default.
- W4313449118 cites W2345985633 @default.
- W4313449118 cites W2379361964 @default.
- W4313449118 cites W2460742184 @default.
- W4313449118 cites W2475873739 @default.
- W4313449118 cites W2488843974 @default.
- W4313449118 cites W2522824586 @default.
- W4313449118 cites W2580284562 @default.
- W4313449118 cites W2783909991 @default.
- W4313449118 cites W2889206679 @default.
- W4313449118 cites W2889540509 @default.
- W4313449118 cites W2952218014 @default.
- W4313449118 cites W3015199127 @default.
- W4313449118 cites W3046234827 @default.
- W4313449118 cites W3205626500 @default.
- W4313449118 cites W4245548524 @default.
- W4313449118 cites W4255333822 @default.
- W4313449118 doi "https://doi.org/10.1190/geo2022-0243.1" @default.
- W4313449118 hasPublicationYear "2023" @default.
- W4313449118 type Work @default.
- W4313449118 citedByCount "0" @default.
- W4313449118 crossrefType "journal-article" @default.
- W4313449118 hasAuthorship W4313449118A5030863883 @default.
- W4313449118 hasAuthorship W4313449118A5039065462 @default.
- W4313449118 hasAuthorship W4313449118A5053725057 @default.
- W4313449118 hasConcept C108583219 @default.
- W4313449118 hasConcept C121332964 @default.
- W4313449118 hasConcept C127313418 @default.
- W4313449118 hasConcept C154945302 @default.
- W4313449118 hasConcept C177212765 @default.
- W4313449118 hasConcept C199360897 @default.
- W4313449118 hasConcept C24890656 @default.
- W4313449118 hasConcept C30399818 @default.
- W4313449118 hasConcept C40375134 @default.
- W4313449118 hasConcept C41008148 @default.
- W4313449118 hasConcept C62520636 @default.
- W4313449118 hasConcept C65682993 @default.
- W4313449118 hasConcept C77088390 @default.
- W4313449118 hasConcept C8058405 @default.
- W4313449118 hasConceptScore W4313449118C108583219 @default.
- W4313449118 hasConceptScore W4313449118C121332964 @default.
- W4313449118 hasConceptScore W4313449118C127313418 @default.
- W4313449118 hasConceptScore W4313449118C154945302 @default.
- W4313449118 hasConceptScore W4313449118C177212765 @default.
- W4313449118 hasConceptScore W4313449118C199360897 @default.
- W4313449118 hasConceptScore W4313449118C24890656 @default.
- W4313449118 hasConceptScore W4313449118C30399818 @default.
- W4313449118 hasConceptScore W4313449118C40375134 @default.
- W4313449118 hasConceptScore W4313449118C41008148 @default.
- W4313449118 hasConceptScore W4313449118C62520636 @default.
- W4313449118 hasConceptScore W4313449118C65682993 @default.
- W4313449118 hasConceptScore W4313449118C77088390 @default.
- W4313449118 hasConceptScore W4313449118C8058405 @default.
- W4313449118 hasFunder F4320321001 @default.
- W4313449118 hasIssue "2" @default.
- W4313449118 hasLocation W43134491181 @default.
- W4313449118 hasOpenAccess W4313449118 @default.
- W4313449118 hasPrimaryLocation W43134491181 @default.
- W4313449118 hasRelatedWork W2081035100 @default.
- W4313449118 hasRelatedWork W2126887587 @default.
- W4313449118 hasRelatedWork W2731899572 @default.
- W4313449118 hasRelatedWork W2939353110 @default.
- W4313449118 hasRelatedWork W3009238340 @default.
- W4313449118 hasRelatedWork W3206324740 @default.
- W4313449118 hasRelatedWork W3215138031 @default.
- W4313449118 hasRelatedWork W4321369474 @default.
- W4313449118 hasRelatedWork W4327774331 @default.
- W4313449118 hasRelatedWork W4360585206 @default.
- W4313449118 hasVolume "88" @default.
- W4313449118 isParatext "false" @default.
- W4313449118 isRetracted "false" @default.
- W4313449118 workType "article" @default.