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Record W1975305236 · doi:10.2118/2002-114-ea

Biogenic Sulfide Production in Continuous Systems: Containment Strategies Targeting Microbial Metabolism

2002· article· en· W1975305236 on OpenAlexafffundabout
Casey R. J. Hubert, Mehdi Nemati, Gerrit Voordouw, G. E. Jenneman

Bibliographic record

VenueCanadian International Petroleum Conference · 2002
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsContainment (computer programming)SulfideProduction (economics)Microbial metabolismChemistryBiochemical engineeringComputer scienceBiologyBacteriaEngineeringEconomics

Abstract

fetched live from OpenAlex

Abstract Nitrate-and nitrite-mediated containment of souring was studied in continuous up-flow packed-bed bioreactors. Produced water of the Coleville oil field in Saskatchewan, Canada, was used as an inoculum for establishing biofilms of sulfate-reducing bacteria (SRB). With a medium containing 12 mM sulfate and 25 mM Nalactate (10–11 mM produced sulfide), 17.5 mM nitrate or 20 mM nitrite were required for complete removal of sulfide at a retention time of 24 h. During nitrate or nitrite addition environmental redox potential increased gradually from -700 mV to -400 mV, with final values still being in the optimum range for SRB activity Prior to addition of nitrate or nitrite microbial communities in the liquid phase were dominated by SRB. Addition of nitrate or nitrite shifted the composition of community to heterotrophic and nitrate-reducing (NRB) bacteria. Introduction Souring, the production of hydrogen sulfide (H2S) in oil reservoirs subjected to water flooding for secondary oil recovery, is caused at least partly by sulfate-reducing bacteria (SRB). These bacteria use electron donors present in the oil and formation water (organic acids and hydrocarbons) to reduce sulfate to sulfide. The activity of SRB is also associated with increased corrosion risk. Strategies for mitigation of biogenic sulfide production include:removal of sulfate from water prior to injection,application of biocides to inhibit or kill SRB, ormanipulation of microbial metabolism through addition of nitrate or nitrite. The latter approach has been tested in the Coleville oil field, located in Saskatchewan, Canada. Injection of 400 ppm ammonium nitrate for 50 days gave on average a 73% decrease in sulfide concentration in injection and producer wells (1). The control of biogenic sulfide production by addition of nitrite, nitrate, and nitrate-reducing, sulfide-oxidizing bacteria (NR-SOB) and the involved mechanisms have been studied in our recent work(2,3). NR-SOB oxidize sulfide (to sulfur or sulfate) when reducing nitrate to nitrite or nitrogen. To better simulate conditions prevailing in oil reservoirs, a study on the containment of souring was conducted in continuous packed-bed bioreactors with established SRB biofilms. The present paper describes the preliminary results of this study. Experimental system and procedures The containment of biogenic H2S production was studied in two up-flow packed-bed bioreactors, made of a glass column (D: 4.5 cm and H: 64 cm), with 5 sampling ports at 14 cm intervals. Sand was used as a matrix for biofilm establishment. Modified CSB medium (2) was introduced at the bottom of the bioreactors, using tygon tubing and a peristaltic pump. The effluent stream left the top of the bioreactor through a rubber stopper and tygon tubing connected to an effluent vessel. The immobilization of bacterial consortia (mainly SRB) was achieved by initial operation of the bioreactors in batch mode, followed by operation in continuous mode. The bioreactors, containing modified CSB medium were inoculated by injecting 10 mL Coleville produced water at each sampling port. The bioreactors were maintained at room temperature (22 °C). Following complete sulfate reduction, the medium was pumped into the bioreactor at an increasing flow rate (final value 9 mL/h, corresponding to a residence time of 24 h).

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 score0.711
Threshold uncertainty score0.977

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 head0.015
GPT teacher head0.213
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 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

Citations0
Published2002
Admission routes3
Has abstractyes

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