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Record W1987359813 · doi:10.2523/iptc-11028-ms

Improved Production From Mature Gas Wells by Introducing Surfactants Into Wells

2005· article· en· W1987359813 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Petroleum Technology Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsGas liftSurface tensionPetroleum engineeringPulmonary surfactantProduction (economics)Natural gas fieldProcess engineeringEnvironmental scienceMaterials scienceNatural gasWaste managementEngineeringChemical engineeringThermodynamics

Abstract

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Abstract A very common problem of mature gas fields is liquid loading. If gas wells suffer from liquid loading production is decreased. If most or all the wells suffer from this problem, then effectively the recovery factor and reserves are reduced. Therefore engineering and operational attention is required to improve operational performance through production enhancement and optimisation. The reason for liquid loading is liquid accumulation in the well bore due to increased liquid gas ratio (LGR) at insufficient gas production rate and gas velocity. Liquid loading is not always obvious but can be verified through the critical velocity and nodal analysis. Theoretical and field investigations deliver a wide range from 5–20 ft/sec for the minimum critical velocity for continuous removal of liquids. There are several production enhancement techniques available to accelerate production and to prolong the effective producing life of gas wells with liquid loading problems. One method is to reduce the effective density and surface tension of the produced fluids by using surfactants as foaming agents. Foaming is an effective method because reservoir energy is utilized. But the application of surfactants to lift liquids has to fit certain gas well production conditions. A key factor is the selection of the most effective surfactant without damaging the reservoir. The important criteria and tests for screening to determine which surfactant works best include, but are not limited to surface tension. Depending on technical and economical limitations various methods of introducing surfactants into the well are used and several possibilities have to be taken into account. Batch or soap sticks for example are not only the simplest methods but also solutions with low initial costs. Nevertheless from a certain point automated and continuous injection should be the preferred solution as for example the installation of a capillary coiled tubing. For managing production decline in mature gas field environments the correct application of surfactants on gas wells with liquid loading is an effective solution. Surfactants are a production optimization opportunity and can be economically used to gain incremental production from depleted gas reservoirs with liquid loading problems.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
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 science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.238
Teacher spread0.232 · 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