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Record W4236478805 · doi:10.2118/2000-083

Biotreatment of Flare Pit Waste

2000· article· en· W4236478805 on OpenAlexafffundabout
P.J.A. Hettiaratchi, P.L. Amatya, R.C. Joshi, G. Ramesh

Bibliographic record

VenueCanadian International Petroleum Conference · 2000
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsUniversity of Calgary
FundersCanadian Association of Petroleum Producers
KeywordsFlareWaste managementEnvironmental scienceEngineeringAerospace engineering

Abstract

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Abstract Flare pits have been used by the upstream oil and gas industry for decades to store and/or burn produced fluids at well sites, compressor stations and batteries. Since produced fluids contain liquid hydrocarbons, process chemicals, crude bitumen or salt water, flare pit (FP) waste usually contains high levels of hydrocarbons, metals, and salts. Bioremediation by land application is the most common method practiced by the oil and gas industry to treat FP waste. High rate slurry phase and solid phase biotreatment methods are viable alternatives to the low cost yet inefficient land treatment option. They can also be used as a rapid biotreatability-screening tool. The slurry phase biotreatment of flare pit waste using 2-L slurry reactor showed an initial decrease in petroleum concentrations, however biodegradation decreased or ceased with time, leaving recalcitrant compounds. The nutrient concentrations (above 350 mg N/L as ammonium nitrogen) did not exhibit statistically significant effects on hydrocarbon degradation. The primary effect of waste composition was highly significant, with higher soil clay content resulting in lower biodegradation. A laboratory solid phase bioremediation study was conducted over a period of 270 days using a statistical partial factorial experimental design. The effects of nitrogen, phosphorus and salinity levels and incubation temperature on the biodegradation of hydrocarbons in the flare pit waste were investigated. A soil contaminated with flare pit hydrocarbons was treated with nitrogen (500, 1250, or 2000 mg/kg of soil), phosphorus (100, 250, or 400 mg/kg of soil) and salt (yielding electrical conductivity of 0, 20, or 40 dS/m) and incubated at three temperatures (20 °, 30 ° and 40 °C). The highest oil and grease (O&G) reduction of 34% was observed in the soils incubated at 30 °C. Soil temperature had more influence on bioremediation rates than did N or P levels. The high P levels, up to 400 mg P/kg soil, had no detrimental effect on hydrocarbon biodegradation. High salinity levels reduced the oil biodegradation rate. Introduction In oil fields around the world, flare pits have been used for decades to store and/or burn produced fluids at older oil and gas well sites, compressor stations and batteries. The produced fluids usually contain a variety of liquid hydrocarbons, process chemicals, crude bitumen and salt water. The usual practice is to store and intermittently burn these produced fluids in earthen pits called "flare pits". The end result is the creation of a highly complex waste known as flare pit (FP) waste. Alberta, the oil province of Canada, is home to about 30,000 flare pit sites.(1) In 1996, the provincial Comparative studies have shown that thermal methods are the most effective (2), but the application of such methods is expensive and poses logistical problems in some locations. For example, in Alberta, the FP sites are scattered over a large geographical area, wherever oil production has taken place. Thermal methods are most cost-effective if used as an ex-situ centralized treatment technique.(2,3) Since FP wastes are found in relatively small quantities scattered over a large number of sites, flexible on-site or in-situ techniques, such as bioremediation, is more cost effective.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score1.000

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)0.0270.001

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 head0.212
Teacher spread0.199 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2000
Admission routes3
Has abstractyes

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