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Record W1964907968 · doi:10.2118/02-09-02

Biotreatment of Flare Pit Waste

2002· article· en· W1964907968 on OpenAlexafffundabout
Prasanna L. Amatya, J. Patrick A. Hettiaratchi, R. C. Joshi

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersAsian Institute of TechnologyCanadian Association of Petroleum ProducersUniversity of Calgary
KeywordsBioremediationEnvironmental remediationSlurryRefineryBiodegradationEnvironmental scienceWaste managementSoil waterSoil contaminationPulp and paper industryEnvironmental chemistryPhosphorusDiammonium phosphateSoil salinityChemistryContaminationEnvironmental engineeringNutrientSoil science

Abstract

fetched live from OpenAlex

Abstract The upstream oil and gas industry has used flare pits (FPs) for decades to store and/or burn produced fluids generated at well sites, compressor stations, and batteries. Since produced fluids contain liquid hydrocarbons, process chemicals, crude bitumen, or salt water, FPs usually contain high levels of hydrocarbons, metals, and salts. At present, 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 application option. The use of slurry-phase and solid-phase biotreatment in the overall strategy for FP waste remediation is reported in this paper. A laboratory solid-phase bioremediation study was conducted over a period of 270 days to investigate the effects of nitrogen, phosphorus, salinity levels, and incubation temperature on the biodegradation of hydrocarbons in FP pit waste employing a statistical partial factorial experimental design. A soil contaminated with flare pit hydrocarbons was treated with nitrogen (500, 1,250, or 2,000 mg/kg of soil), phosphorus (100, 250, or 400 mg/kg of soil), and salt (yielding electrical conductivities 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. The high P levels, up to 400 mg P/kg soil, had no detrimental effect on hydrocarbon biodegradation. High salinity levels reduced the rate of hydrocarbon biodegradation. The slurry-phase biotreatment of flare pit waste using 2 L slurry reactors showed an initial rapid decrease in hydrocarbon concentrations. However, biodegradation decreased with time and eventually ceased, 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. Introduction The produced fluids generated at oil and gas well sites, compressor stations, and batteries contain a variety of liquid hydrocarbons, process chemicals, crude bitumen, and salt water. Until recently, the industry practice was to store and intermittently burn these produced fluids in earthen pits called "flare pits." According to recent estimates, Alberta is home to about 30,000 flare pit (FP) sites(1). In 1996, the provincial government of Alberta banned the disposal of produced fluids in FPs, and requested the oil and gas industry to remediate former FP sites that are posing significant risks to human health and the surrounding environment. Because of the highly complex and variable nature of the waste, remediation of FP waste presents a serious challenge. At present, the most common technique used for remediation of FP sites is bioremediation by land application. Land application is relatively low-tech and less expensive (compared with competing techniques such as incineration). Furthermore, land application is popular in locations such as rural Alberta, because of the nature of the spatial distribution of FP sites.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
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 head0.006
GPT teacher head0.169
Teacher spread0.163 · 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 designSimulation or modeling
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

Citations30
Published2002
Admission routes3
Has abstractyes

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