Using microseismicity to map cotton valley hydraulic fractures
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2000Using microseismicity to map cotton valley hydraulic fracturesAuthors: Theodore I. UrbancicJames RutledgeTheodore I. UrbancicEngineering Seismology Group Canada Inc and James RutledgeLos Alamos National Laboratoryhttps://doi.org/10.1190/1.1815676 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1815676FiguresReferencesRelatedDetailsCited ByImaging below a complex overburden with borehole-seismic dataJakob B. U. Haldorsen and Leif Jahren18 March 2020 | GEOPHYSICS, Vol. 85, No. 3Velocity analysis and subsurface source location improvement using moveout-corrected gathersAriel Lellouch and Moshe Reshef1 April 2019 | GEOPHYSICS, Vol. 84, No. 3The Role of Moment Tensors in the Characterization of Hydraulic Stimulations13 May 2018Characterizing Reservoir Behavior with Cluster-Based Microseismic AnalysisAdam M. Baig, Ted Urbancic, Katie Bosman, Ellie Ardakani, and John M. Thompson12 September 2017References31 August 2017Improved methods for detection and arrival picking of microseismic events with low signal-to-noise ratiosYuyang Tan and Chuan He22 February 2016 | GEOPHYSICS, Vol. 81, No. 2A microseismic experiment in Abu Dhabi, United Arab Emirates: implications for carbonate reservoir monitoring16 August 2013 | Arabian Journal of Geosciences, Vol. 7, No. 9Locating microseismic sources using migration-based deconvolutionJakob B. U. Haldorsen, Nicholas J. Brooks, and Mathieu Milenkovic28 August 2013 | GEOPHYSICS, Vol. 78, No. 5Locating Microseismic Events using Migration-based DeconvolutionJ.B.U. Haldorsen, M. Milenkovic, N. Brooks, C. Crowell, and M.B. Farmani25 October 2012In situ monitoring of rock fracturing using shear wave splitting analysis: an example from a mining setting23 September 2011 | Geophysical Journal International, Vol. 187, No. 2Petroleum reservoir characterization using downhole microseismic monitoringS. C. Maxwell, J. Rutledge, R. Jones, and M. Fehler14 September 2010 | GEOPHYSICS, Vol. 75, No. 5Interpretation of Microseismicity Resulting from Gel and Water Fracturing of Tight Gas Reservoirs18 November 2009 | Pure and Applied Geophysics, Vol. 167, No. 1-2Single Versus Multiwell Microseismic Recording: What Effect Monitoring Configuration Has On InterpretationMargeret Seibel, Adam Baig, and Ted Urbancic21 October 2010Automated seismic event location for hydrocarbon reservoirsComputers & Geosciences, Vol. 29, No. 7Hydraulic stimulation of natural fractures as revealed by induced microearthquakes, Carthage Cotton Valley gas field, east TexasJames T. Rutledge and W. Scott Phillips4 April 2003 | GEOPHYSICS, Vol. 68, No. 2 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: 04 Jan 2005 CITATION INFORMATION Theodore I. Urbancic and James Rutledge, (2000), "Using microseismicity to map cotton valley hydraulic fractures," SEG Technical Program Expanded Abstracts : 1444-1448. https://doi.org/10.1190/1.1815676 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".