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Record W4253317948 · doi:10.2118/2004-104

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

2004· article· en· W4253317948 on OpenAlexaff
J.A. Becaria, P. R. Toma, E. Kuru

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGas liftPetroleum engineeringLift (data mining)Materials scienceMechanicsEnvironmental scienceMarine engineeringComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Production of fluids from low-pressure reservoirs requires a continuous or an intermittent artificial lifting technology. If the shut-in fluid level is less than 20% of the depth of the well finding a suitable and economic artificial lifting technology is a challenging task. Depending on local conditions and economics, gas or steam lifting alone or associated with other artificial lifting technologies is selected. Within a limited range of gas-liquid flowrates, use of smalldiameter gas lifting technology is better suited than the gas lifting using conventional tubing to produce liquids from lowpressure reservoirs. Laboratory investigations dedicated to small-diameter gas lifting operations have been so far limited to fluid transfer operations requiring maximum 10–20 m. This study responds to the industry need for a better evaluation of depth - diameter flowrates limitations in view of assessing the potential field application of gas lifting for very low reservoir pressures and relatively small liquid flowrates. Production of oil and gas from pressure-depleted reservoirs, recovery of methane from coalbed reserves, and efficient drainage of heavy oil and saturated high-temperature condensate produced under steam-assisted gravity methods, where reservoir pressure is marginally low, require a revisiting of conventional artificial lifting technologies. For example, there are thousands of dormant gas wells where bottom water aquifers of 50 m or less impede gas production. Similar conditions are often found in the coalbed methane reservoirs. Use of submersible electric pumps for lowpressure, low liquid production reservoirs is rarely an economic or a viable technical option. The availability of gas and the relatively small amount of liquid to be transferred suggest gas lifting as a potential strategy for producing the reservoir water and releasing the gas. However, conventional gas lifting (using tubes with a diameter D>1 in.) is not possible due to the extreme low reservoir pressure conditions. Small diameter pipes (D<1 in.) were occasionally used for gas lifting operations in such fields with mixed results. In this paper, a critical review of the existing literature on the numerical evaluation methods of gas lifting was presented first. Laboratory tests were conducted by using a specially designed rig and the results were used to evaluate the accuracy of the existing model predictions. Experimental results were also used for assessing the effect of gas-liquid flowrate and interfacial tension on the liquid production rate and flowing bottomhole pressure. Experimental data were further used for developing a model to determine critical limit of the small diameter gas lifting technique under field conditions. The new model, better adapted for the needs of the industry, can be used to transfer laboratory information to the field scale. Introduction Gaslifting or airlift has been used to remove water from flooded mines since 17821–2. Today, natural gaslifting is commonly used for oil wells where gas and liquid are produced together. Conventional gaslifting uses tubing (or ducts) with a diameter greater than 1 in. Vertical upward transport of gas and liquid for such conditions is well investigated and both empirical3 and mechanistic models are available.4–6

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 score0.315
Threshold uncertainty score0.964

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 head0.018
GPT teacher head0.213
Teacher spread0.195 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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