The applications of amplitude versus offset in carbonate reservoir: Re‐examining the potential
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2000The applications of amplitude versus offset in carbonate reservoir: Re‐examining the potentialAuthors: Yongyi LiJonathan DowntonYongyi LiScott Pickford and Jonathan DowntonScott Pickfordhttps://doi.org/10.1190/1.1815738 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1815738FiguresReferencesRelatedDetailsCited byHTD, high trace density seismic survey collects real data in the near offsets and tight spatial sampling in X, Y, Z and azimuthal domains for the purpose of determining accurate seismic attributes, elastic properties, and detailed geologic heterogeneities: Fasken C-Ranch and Mud City 3D survey, Permian BasinBruce Karr, Andrew Lewis, and Ron Bianco1 September 2021Estimation of Shallow Sulphur Deposit Resources Based on Reflection Seismic Studies and Well Logging27 August 2021 | Energies, Vol. 14, No. 17Fluid detection in carbonate rocks by integrating well logs and seismic attributesMohammad Reza Saberi8 November 2019 | Interpretation, Vol. 8, No. 1Simultaneous Inversion of Shallow Seismic Data for Imaging of Sulfurized Carbonates28 March 2019 | Minerals, Vol. 9, No. 4Identification of fractured carbonate vuggy reservoirs in the S48 well area using 3D 3C seismic technique: A case history from the Tarim BasinZongjie Li, Yun Wang, Zichuan Yang, Haiying Li, and Guangming Yu21 December 2018 | GEOPHYSICS, Vol. 84, No. 1Pre-stack inversion for caved carbonate reservoir prediction: A case study from Tarim Basin, China8 December 2011 | Petroleum Science, Vol. 8, No. 4Combined Bayesian AVO inversion with rock physics to predict gas carbonate reservoirLuanxiao Zhao, Jianhua Geng, Jiubing Cheng, De‐hua Han, and Tonglou Guo8 August 2011Inversion and interpretation of a 3D seismic data set from the Ouachita Mountains, OklahomaAbuduwali Aibaidula and George McMechan20 February 2009 | GEOPHYSICS, Vol. 74, No. 2Characterization of heterogeneities in CarbonatesRavi Sharma and Monika Prasad14 October 2009On the applicability of Gassmann model in carbonatesRavi Sharma, Manika Prasad, Ganpat Surve, and G. C. Katiyar6 October 2006Seismic impedance inversion and interpretation of a gas carbonate reservoir in the Alberta Foothills, western CanadaGislain B. Madiba and George A. McMechan25 September 2003 | GEOPHYSICS, Vol. 68, No. 5 SEG Technical Program Expanded Abstracts 2000ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2000 Pages: 2484 publication data© 2000 Copyright © 2000 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 04 Jan 2005 CITATION INFORMATION Yongyi Li and Jonathan Downton, (2000), "The applications of amplitude versus offset in carbonate reservoir: Re‐examining the potential," SEG Technical Program Expanded Abstracts : 166-169. https://doi.org/10.1190/1.1815738 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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".