VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log Images
Raster logs are scanned representations of the analog data recorded in subsurface drilling. Geologists rely on these images to interpret well-log curves and deduce the physical properties of geological formations. Scanned images contain various artifacts, including hand-written texts, brightness var...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/9/7/136 |
| _version_ | 1797588776551186432 |
|---|---|
| author | M. Quamer Nasim Narendra Patwardhan Tannistha Maiti Stefano Marrone Tarry Singh |
| author_facet | M. Quamer Nasim Narendra Patwardhan Tannistha Maiti Stefano Marrone Tarry Singh |
| author_sort | M. Quamer Nasim |
| collection | DOAJ |
| description | Raster logs are scanned representations of the analog data recorded in subsurface drilling. Geologists rely on these images to interpret well-log curves and deduce the physical properties of geological formations. Scanned images contain various artifacts, including hand-written texts, brightness variability, scan defects, etc. The manual effort involved in reading the data is substantial. To mitigate this, unsupervised computer vision techniques are employed to extract and interpret the curves digitally. Existing algorithms predominantly require manual intervention, resulting in slow processing times, and are erroneous. This research aims to address these challenges by proposing VeerNet, a deep neural network architecture designed to semantically segment the raster images from the background grid to classify and digitize (i.e., extracting the analytic formulation of the written curve) the well-log data. The proposed approach is based on a modified UNet-inspired architecture leveraging an attention-augmented read–process–write strategy to balance retaining key signals while dealing with the different input–output sizes. The reported results show that the proposed architecture efficiently classifies and digitizes the curves with an overall F1 score of 35% and Intersection over Union of 30%, achieving 97% recall and 0.11 Mean Absolute Error when compared with real data on binary segmentation of multiple curves. Finally, we analyzed VeerNet’s ability in predicting Gamma-ray values, achieving a Pearson coefficient score of 0.62 when compared to measured data. |
| first_indexed | 2024-03-11T00:56:50Z |
| format | Article |
| id | doaj.art-827e5b7252b6417ab326414b3173306f |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-11T00:56:50Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-827e5b7252b6417ab326414b3173306f2023-11-18T19:57:01ZengMDPI AGJournal of Imaging2313-433X2023-07-019713610.3390/jimaging9070136VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log ImagesM. Quamer Nasim0Narendra Patwardhan1Tannistha Maiti2Stefano Marrone3Tarry Singh4Deepkapha AI Research, Street Vaart ZZ n° 1.d, 9401 GE Assen, The NetherlandsDeepkapha AI Research, Street Vaart ZZ n° 1.d, 9401 GE Assen, The NetherlandsDeepkapha AI Research, Street Vaart ZZ n° 1.d, 9401 GE Assen, The NetherlandsDepartment of Electrical Engineering and Information Technologies (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDeepkapha AI Research, Street Vaart ZZ n° 1.d, 9401 GE Assen, The NetherlandsRaster logs are scanned representations of the analog data recorded in subsurface drilling. Geologists rely on these images to interpret well-log curves and deduce the physical properties of geological formations. Scanned images contain various artifacts, including hand-written texts, brightness variability, scan defects, etc. The manual effort involved in reading the data is substantial. To mitigate this, unsupervised computer vision techniques are employed to extract and interpret the curves digitally. Existing algorithms predominantly require manual intervention, resulting in slow processing times, and are erroneous. This research aims to address these challenges by proposing VeerNet, a deep neural network architecture designed to semantically segment the raster images from the background grid to classify and digitize (i.e., extracting the analytic formulation of the written curve) the well-log data. The proposed approach is based on a modified UNet-inspired architecture leveraging an attention-augmented read–process–write strategy to balance retaining key signals while dealing with the different input–output sizes. The reported results show that the proposed architecture efficiently classifies and digitizes the curves with an overall F1 score of 35% and Intersection over Union of 30%, achieving 97% recall and 0.11 Mean Absolute Error when compared with real data on binary segmentation of multiple curves. Finally, we analyzed VeerNet’s ability in predicting Gamma-ray values, achieving a Pearson coefficient score of 0.62 when compared to measured data.https://www.mdpi.com/2313-433X/9/7/136raster logdigitizationtransformerdeep learningwell-log curves |
| spellingShingle | M. Quamer Nasim Narendra Patwardhan Tannistha Maiti Stefano Marrone Tarry Singh VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log Images Journal of Imaging raster log digitization transformer deep learning well-log curves |
| title | VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log Images |
| title_full | VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log Images |
| title_fullStr | VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log Images |
| title_full_unstemmed | VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log Images |
| title_short | VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log Images |
| title_sort | veernet using deep neural networks for curve classification and digitization of raster well log images |
| topic | raster log digitization transformer deep learning well-log curves |
| url | https://www.mdpi.com/2313-433X/9/7/136 |
| work_keys_str_mv | AT mquamernasim veernetusingdeepneuralnetworksforcurveclassificationanddigitizationofrasterwelllogimages AT narendrapatwardhan veernetusingdeepneuralnetworksforcurveclassificationanddigitizationofrasterwelllogimages AT tannisthamaiti veernetusingdeepneuralnetworksforcurveclassificationanddigitizationofrasterwelllogimages AT stefanomarrone veernetusingdeepneuralnetworksforcurveclassificationanddigitizationofrasterwelllogimages AT tarrysingh veernetusingdeepneuralnetworksforcurveclassificationanddigitizationofrasterwelllogimages |