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Record W1968492254 · doi:10.2118/2005-159

Produced Fluids Separation Using A Coalescer Column

2005· article· en· W1968492254 on OpenAlexafffundabout
G. Renouf, L. Kurucz, D.R. Soveran

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)
FundersPetroleum Technology Research Centre
KeywordsCitationColumn (typography)SettlingWater columnComputer scienceEnvironmental scienceLibrary scienceGeologyTelecommunicationsEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract A coalescer column - a simple, inexpensive, and environmentally friendly technology - successfully removed water from produced heavy oil emulsions. The laboratory study tested the use of the column, and the effects of column length, column packing size, temperature, flow rate, demulsifier concentration, and water addition. The use of the column improved basic sediment and water (BS&W) values after 4 hours of settling time by an average of 38%. Flowing the emulsions through the column at lower temperatures and much lower demulsifier concentrations matched the results of conventional treating. The results indicated that incorporating a coalescer column into a treatment facility allowed the reduction of demulsifier concentration from 250 ppm to 70 ppm, translating to an annual cost savings of $320,000 to $1,100,000 per battery. The column also promoted faster treating. Water droplets grew by as much as 34%, suggesting that treating time could be sped up by an average of 21% and as high as 80%. Introduction Heavy oil producers have reported that chemical costs represent the highest fraction of their operating expenses. A small survey of battery operators showed that demulsifier concentrations at heavy oil batteries ranged from 200 to 333 ppm (1 L/5 m3 to 1 L/3 m3) in 2001. 1 These operators also reported that demulsifier doses were rising: within the last five years, the concentration of demulsifier used to treat a typical heavy oil has risen by 25 to 50% for many reasons. A battery in the heavy oil region can easily spend over $100,000 annually on demulsifying chemicals. The two treating batteries which supplied emulsion samples for this set of experiments were estimated to spend between $400,000 and $1,100,000 every year on such costs. Heating costs are also considerable: some heavy oil batteries heat their pressurized treating vessels to 130 °C. In addition to the cost of heating, producers are becoming increasingly aware of the importance of reducing greenhouse gas production. An alternate, inexpensive, and environmentally friendly technology to help separate oil and water would be highly desirable. One promising separation method is of the passive mechanical type: a coalescer column. Over the past 18 years, the Saskatchewan Research Council (SRC) and the University of Regina have applied this technique to break water-in-oil emulsions. 2–8 Whereas our previous work focused on resolving slop oils, this study applied the coalescer column to the treatment of wellhead emulsions. A literature survey on the subject of coalescers and resolving crude oil emulsions is deceptive. A number of researchers use the word coalescer, but apply it to plate separators or pipes with no packing. Our use of the term restricts it to a pipe filled with some sort of porous packing material which aids in the coalescence of dispersed droplets of an emulsion. In their review of the literature on coalescing media, Stocker et al. listed the many types of packing materials that have been tested. 9 These fall into the categories of fixed media, granular packing, and fiber packing.

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 score0.403
Threshold uncertainty score0.912

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 head0.019
GPT teacher head0.261
Teacher spread0.243 · 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

Citations2
Published2005
Admission routes3
Has abstractyes

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