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Record W2090768176 · doi:10.2118/115633-ms

Chemical Foamers for Gas Well Deliquification

2008· article· en· W2090768176 on OpenAlexaff
Martin J. Willis, David I. Horsup, Duy Nguyen

Bibliographic record

VenueSPE Asia Pacific Oil and Gas Conference and Exhibition · 2008
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsNalco (Canada)
Fundersnot available
KeywordsCasingPetroleum engineeringGas liftSurface tensionOil wellHydrostatic pressureProcess (computing)Coiled tubingProcess engineeringMechanical engineeringEnvironmental scienceEngineeringMechanicsComputer science

Abstract

fetched live from OpenAlex

Abstract There is a noticeable impact on a gas well's production rate when there is a build up of liquids downhole in the casing and the tubing of the well. The liquid may be in the form of formation water, condensed water or hydrocarbon condensate, but coupled with a fall in reservoir pressure the affect on production is the same. It causes the flow regime of the well to change, to an eventual point when the well ceases to produce. This is due to a hydrostatic lock, typically in the tubing, that the well pressure is unable to move. To reduce the production decline and prevent premature well shut in chemical foamers can be used as a means of artificial lift. These are surfactant chemistries designed to modify physical properties of the liquids, such as surface tension, which aid unloading. The common chemistries used are discussed in this paper and a detailed explanation provided on how they are effective at deliquifying gas wells. To develop this technology for commercial use it is important to have a process for evaluating performance. This begins in the laboratory using the unloading rig. This is a test method that provides information on the foam generated as well as indicate an unloading potential. Once a potential candidate has been identified in the lab then the process is undertaken to transfer the technology to the field. Identifying a suitable well to enable a field trial to be completed. Within the paper the test method is explained and the transfer process and results from the field presented.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.538

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)0.0000.000

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 head0.014
GPT teacher head0.196
Teacher spread0.182 · 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 designOther design
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

Citations27
Published2008
Admission routes1
Has abstractyes

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