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Record W2055986419 · doi:10.3997/2214-4609.201411961

Permanent Seismic Reservoir Monitoring for Real-time Surveillance of Thermal EOR at Peace River

2015· article· en· W2055986419 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsSoftware deploymentProfitability indexSAFERFootprintCarbon footprintProduction (economics)Environmental scienceGeologyComputer scienceComputer securityGreenhouse gasBusiness

Abstract

fetched live from OpenAlex

Summary Permanent seismic reservoir monitoring (PSRM) solutions, if of high enough sensitivity and low enough cost, can be used to tackle the many known problems faced by seismic monitoring onshore and thereby increase the profitability of such developments. Here we focus on thermal EOR monitoring using continuous seismic, as provided by SeisMovie®, a registered trademark of CGG. We review the PSRM staircase that Shell has climbed since 2009, introduce the most areally extensive deployment at Peace River in Alberta, Canada, and discuss some of the initial findings and plans ahead. We show progress with PSRM to generate better onshore data that will lead to higher recovery, higher production, and safer and cleaner operations. Significant steps were made towards on-demand, lower footprint PSRM, and next steps are set towards cheaper, non-intrusive automated systems that are required for broad application.

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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.277
Teacher spread0.246 · 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