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Record W2972438884 · doi:10.2118/197060-pa

Application of Geochemical and Isotopic Tracers for Characterization of SAGD Waters in the Alberta Oil-Sand Region

2019· article· en· W2972438884 on OpenAlexaffabout
Christine Ciszkowski, Zied Ouled Ameur, Jeffrey P. J. Forsyth, Mike Nightingale, Maurice Shevalier, Bernhard Mayer

Bibliographic record

VenueSPE Production & Operations · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsCenovus Energy (Canada)University of Calgary
Fundersnot available
KeywordsOil sandsSteam-assisted gravity drainageGeologyAsphaltδ34Sδ18OPrecipitationMeteoric waterPetroleumStable isotope ratioGeochemistryPetroleum engineeringHydrology (agriculture)Environmental scienceGroundwaterGeotechnical engineering

Abstract

fetched live from OpenAlex

Summary Mineral precipitation (scale) can significantly hinder production in petroleum reservoirs. This includes steam-assisted-gravity-drainage (SAGD) operations used for bitumen recovery in the Athabasca oil-sand region (AOSR) of northeastern Alberta, Canada. We explored whether select geochemical and isotope tracers (δ2H, δ18O, δ11B, δ34S, δ13C, 87Sr/86Sr) in SAGD-water sources can help to improve the understanding of the dynamics of reservoir fluids and their mixing in SAGD operations that might contribute toward scale precipitation. Pore water, bottom-formation water, steam condensate, and returned emulsions (produced bitumen and water) were sampled from an SAGD reservoir in northeastern Alberta and analyzed for geochemical and isotopic parameters. The results obtained indicate distinct Na and Cl concentrations and δ18O and δ2H values for these fluid sources. Significant differences in δ13CDIC, δ11B, and δ34S values and 87Sr/86Sr ratios were observed between bottom-formation water, steam condensate, and returned-water samples and hence constitute excellent tracers for bottomwater (BW) influx.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.187

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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 designBench or experimental
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
Published2019
Admission routes2
Has abstractyes

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