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Record W2056148417 · doi:10.2118/06-06-03

Small-Diameter Gas Lift Systems-A Potential Technical Solution for Transport of Fluids From Low-Pressure Reservoirs

2006· article· en· W2056148417 on OpenAlexafffund
J. Becaria, P. Toma, Ergün Kuru

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsUniversity of Alberta
FundersNational Research Council CanadaUniversidad Industrial de Santander
KeywordsSucker rodArtificial liftPetroleum engineeringGas liftVolumetric flow rateLift (data mining)Natural gasFlow (mathematics)Environmental scienceEngineeringInjectorFluid dynamicsMechanical engineeringMechanicsWaste managementComputer science

Abstract

fetched live from OpenAlex

Abstract Continuous or intermittent artificial lifting technology is competing today with electrical submersible progressive cavity (PC) pumping and sucker rod pumping for producing fluids from lowpressure reservoirs. If the shut-in fluid level is less than 45% of the depth of the well, finding a suitable and economic artificial lifting technology is a challenging task. There are thousands of dormant gas wells where bottom water accumulations of 50 m or less impede gas production. Similar conditions are often found in coalbed methane reservoirs. Due to variable (and shallow) water levels and gas presence, rod pumping cannot be used and submersible electric pumps often pose operational problems. Depending on local conditions and economics, gas lifting, alone or associated with other artificial lifting technologies, can be used for producing such reservoirs. Within a limited range of gas-liquid flow rates, depth, and reservoir pressure, the use of small-diameter pipes for gas lifting technology can become a viable technology. Laboratory investigations dedicated to small-diameter gas lifting operations have been so far limited to fluid transfer operations requiring a maximum of 10 - 20 m. This study uses mechanistic modelling approaches to respond to the industry's need for a better evaluation of depth/diameter flow rate limitations in view of assessing potential field applications of gas lifting for low reservoir pressures and relatively small liquid flow rates. Laboratory tests were conducted in a specially designed rig. Experimental results were used to evaluate the accuracy of the existing model predictions and for assessing the effect of injected gas flow rates, reservoir pressure, and liquid interfacial tension on the liquid production rates. To improve predictions of existing mechanistic models, particularly for small-diameter tubings and low pressure reservoir conditions, a new model is proposed and compared first with experimental results. The new model is then used as a scaling tool for assessing critical field depth conditions. Introduction Gas lifting or air lift has been used to remove water from flooded mines since 1782(1, 2). Today, natural gas lifting is commonly used for oil wells where gas and liquid are produced together. Conventional gas lifting uses tubing (or ducts) with a diameter greater than 2.54 cm. Vertical upward transport of gas and liquid for such conditions is well investigated and both empirical(3) and mechanistic models are available(4–6). During the last 20 years, mechanistic models are gaining more acceptance, replacing empirical models. Development of a mechanistic model involves:extensive visual observations of field and laboratory-scale models in view of assessing specific boundaries of flow patterns (e.g., bubble, slug, annular, stratified, etc.);assessment of the main gas-liquid features (e.g., bubbles, liquid film, etc.) and of the phases interface aspect (e.g., smooth, wavy, etc);estimation of local gas and liquid velocities, including the slip;estimations of local void fraction and of static and dynamic pressures;computer-assisted integration of "local" features to include the pipe pressure-volume-temperature (axial) profile; and,field validation. Flow pattern mapping and the drift-flux model(7) are essential tools used to evaluate the gas-liquid relative velocities and local void fraction.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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 designBench or experimental
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
Published2006
Admission routes2
Has abstractyes

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