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Record W2061560674 · doi:10.2118/141354-ms

Souring Treatment with Nitrate in Fields from which Oil is Produced by Produced Water Reinjection

2011· article· en· W2061560674 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSPE International Symposium on Oilfield Chemistry · 2011
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes for Water ResourcesBaker Hughes
KeywordsNitrateSulfideSulfateOil fieldCorrosionEnvironmental scienceProduced waterEnvironmental chemistryWater treatmentPetroleum engineeringChemistryEnvironmental engineeringGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Nitrate can control souring in fields with high bottom hole temperature (BHT) and where sulfide is produced in the near-injection-wellbore-region (NIWR). The objective of the treatment is to lower the sulfide concentration in produced water and oil, reducing corrosion risk in producing wells and above-ground infrastructure. Achieving this objective can be problematic for fields with low BHT or for fields in which the reservoir contributes sulfate to the produced water, as is demonstrated by analysing three PWRI case studies. Nitrate was found to effectively oxidize sulfide in produced waters, even when excess oil organics were present. An alternative strategy that should be considered is, therefore, to inject nitrate in the produced waters in a dose corresponding to the sulfide concentration.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.639

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)0.0010.000

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 head0.013
GPT teacher head0.221
Teacher spread0.208 · 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