Application of the radial basis function neural network to the prediction of log properties from seismic attributes — A channel sand case study
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2003Application of the radial basis function neural network to the prediction of log properties from seismic attributes — A channel sand case studyAuthors: Brian H. RussellDaniel P. HampsonLaurence R. LinesBrian H. RussellHampson‐Russell Software Services Ltd., Daniel P. HampsonHampson‐Russell Software Services Ltd., and Laurence R. LinesDepartment of Geology and Geophysics, University of Calgaryhttps://doi.org/10.1190/1.1817949 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1817949FiguresReferencesRelatedDetailsCited ByA novel method for prediction of flowing pressure of multi-stage fracturing horizontal wellAIP Advances, Vol. 11, No. 7Surrogate Reservoir Model for Average Reservoir Pressure20 August 20173D seismic attributes and well-log facies analysis for prospect identification and evaluation: Interpreted palaeoshoreline implications, Weirman Field, Kansas, USAJournal of Petroleum Science and Engineering, Vol. 1333D Seismic attributes analysis to outline channel facies and reveal heterogeneous reservoir stratigraphy: Weirman Field, Ness County, Kansas, USAAbdelmoneam Raef, Matthew Totten, Charlotte Perdew, and Mazin Abbas21 October 2010Application of Artificial Neural Networks to Predicate Shale Content SEG Technical Program Expanded Abstracts 2003ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2003 Pages: 2452 publication data© 2003 Copyright © 2003 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 03 Jan 2005 CITATION INFORMATION Brian H. Russell, Daniel P. Hampson, and Laurence R. Lines, (2003), "Application of the radial basis function neural network to the prediction of log properties from seismic attributes — A channel sand case study," SEG Technical Program Expanded Abstracts : 454-457. https://doi.org/10.1190/1.1817949 Plain-Language Summary PDF DownloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".