MétaCan
Menu
← Back to cohort
Record W2515524697 · doi:10.1190/segam2016-13973145.1

Seismic modeling and imaging for a shallow CO<sub>2</sub> injection project

2016· article· en· W2515524697 on OpenAlexaff
Davood Nowroozi, Don C. Lawton, Hassan Khaniani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsNexen (Canada)University of Calgary
Fundersnot available
KeywordsAmplitudeGeologyGaussianPlumeGeophysical imagingReflection (computer programming)WaveformPresentation (obstetrics)Point (geometry)Saturation (graph theory)SeismologyAcousticsComputer scienceOpticsPhysicsGeometryMathematicsMeteorologyTelecommunications

Abstract

fetched live from OpenAlex

We investigate the effects of CO2 storage monitoring with seismic imaging using fluid simulation, rock physics and wavefield propagation. The fluid simulation is used to estimate the saturation of CO2 and reservoir pressure. The influence of saturated CO2 on rock elastic properties are approximated using Gassmann’s equation. Using a numerical example, we show CO2 flow simulation plume create the Gaussian shaped varition of the elastic properties around CO2 injection point. The result of wave propagation and reverse time migration for a time-lapse study are compared with a scatterpoint method which has non-Gaussian shaped (i.e. a sharp contrast). The results shows that the amplitude of forward modeling and RTM image of injection zone is smaller compared to the scatter point models. This suggests the use of alternative approach such as traveltime variation as compared to reflection imaging. Presentation Date: Wednesday, October 19, 2016 Start Time: 1:55:00 PM Location: 156 Presentation Type: ORAL

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.235
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicSeismic Imaging and Inversion Techniques→French-language works237,207→