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Record W3126074197 · doi:10.3997/2214-4609.202011434

Three-Axis Borehole Gravity Feasibility Method and its Application to CO2 Storage Monitoring

2021· article· en· W3126074197 on OpenAlexaboutno aff
Z. Du, Richard Krahenbuhl, Adrian Topham, Jeremy Lofts, Y. Li, Ashwin A. Seshia, Tony Espie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWirelineBoreholeGravimeterGeologyPlumeGeophysicsPetroleum engineeringGeodesyGeotechnical engineeringEngineeringMeteorologyPhysics

Abstract

fetched live from OpenAlex

Summary Geophysical monitoring of underground CO2 injection forms an important component of a carbon capture and storage program. Until recently, seismic methods were used to map the spreading of CO2 plume and are the dominant technique used for monitoring. However, there is a disadvantage in use of seismic whose relationship with fluid saturation are interpreted based on the empirical rock physics models using effective medium theory and often only known by statistical methods with large error bars. Gravity measurements have a unique advantage among many geophysical methods that the mass density change being detected by the gravity within a reservoir is directly and uniquely related to the dynamic fluid redistribution. In this paper the status of a program to develop a wireline deployable three-axis borehole gravity sensor with a target sensitivity of ~ 5 μ Gal is firstly introduced, using a resonant MEMS (Microelectromechanical systems) vibrating beam technology innovation. Followed by a feasibility study for monitoring of CO2 in a deep reservoir at the Aquistore storage site in Canada. We model and predict the gravity variation due to density changes during a period of CO2 injection. This study demonstrates the pre-survey feasibility modelling of the emerging field of timelapse gravity monitoring.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.031
GPT teacher head0.287
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2021
Admission routes1
Has abstractyes

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