MétaCan
Menu
Back to cohort
Record W2014586467 · doi:10.2118/2003-144-ea

Oil Field Biotechnology: Should We Use Nitrate or Nitrite to Remediate Souring

2003· article· en· W2014586467 on OpenAlexafffundabout
Gerrit Voordouw

Bibliographic record

VenueCanadian International Petroleum Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaConocoPhillips
KeywordsNitrateNitriteOil fieldEnvironmental scienceWaste managementBiotechnologyBiochemical engineeringEnvironmental chemistryPetroleum engineeringChemistryEngineeringBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The application of nitrate or nitrite to remediate souring during waterflooding is proven technology with the objective of (i) removing sulfide produced by sulfatereducing bacteria (SRB) and (ii) inhibiting further sulfide production by the SRB. Nitrate or nitrite injection stimulates heterotrophic, nitrate-reducing bacteria (hNRB) and nitrate-reducing, sulfide oxidizing bacteria (NR-SOB). In fields with SRB, but little or no hNRB and NR-SOB activity the use of nitrite is preferable, because nitrite directly inhibits SRB. Nitrate has no effect under these conditions. In fields with all 3 bacterial groups use of nitrate is preferred, because it has more oxidative power. The nitrate dose required is then determined largely by the concentration of degradable oil organics. Unfortunately this concentration is generally unknown. Introduction Oil fields that are subject to waterflooding can experience SRB-mediated hydrogen sulfide production over time (souring), especially when sea water wich has a high sulfate content water (∼30 mM) is used for injection For instance in 1994 total H2S production from the Skjold field in the Danish sector of the North Sea was 100 kg/day, whereas in 2000 this had increased to 700 kg/day, with occasional surges to 1100 kg/day(1). Because sulfide is toxic and corrosive it needs to be removed by chemical treatment e.g. amine scrubbing. A second negative aspect of souring is that it can reduce reservoir permeability by precipitating soluble metal ions as the sulfides. Souring can be reduced by biocide treatment. Supplementing injection water in the Skjold field periodically with tetrakishydroxymethylphosphonium sulfate (THPS) caused dips in H2S production. In the Velsefrikk field in the Norwegian sector of the North Sea glutaraldehyde was injected biweekly from 1989 to 1999(2). In Alberta and Saskatchewan oil reservoirs diamines are used routinely to reduce souring and corrosion associated with SRB growth in topsides equipment. Instead of using toxic biocides, the use of nitrate or nitrite has been advocated as a less hazardous, environmentally friendly alternative. The application of nitrates and their mechanism of action are discussed in this paper. EXAMPLES OF APPLICATION OF NITRATES The use of nitrates to reduce souring in offshore operations in the North Sea is well-documented. In the case of the Velsefrikk field biocides were completely replaced with continuous injection of 30 ppm nitrate since 1999, resulting in decreased souring and corrosion(2). Injection of 150 to 250 ppm nitrate for 3 months in the Skjold field was partially successful with 80% sulfide removal from fractured areas and lower levels of sulfide removal from areas of reduced permeability(1). In view of this limited success the use of THPS was resumed. Use of nitrite, instead of nitrate, gave successful souring control of gas wells for up to 7 months and of oil wells for up to 1 month following treatment(3). It was claimed that oil production increased following nitrite treatment due to dissolution of solid sulfides that were blocking the injection flow path(3). Closer to home injection of 400 ppm nitrate reduced sulfide concentrations in injector and producing wells by 73% in a field test in the Coleville field near Kindersley (SK)(4).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.956

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 head0.040
GPT teacher head0.238
Teacher spread0.198 · 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 designNot applicable
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

Citations2
Published2003
Admission routes3
Has abstractyes

Explore more

Same venueCanadian International Petroleum ConferenceSame topicAnaerobic Digestion and Biogas ProductionFrench-language works237,207