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Record W4297579214 · doi:10.2118/209720-ms

Jet Lift Bridges Transition Gaps Between Various Forms of Artificial Lift in Horizontal Well Lifecycle

2022· article· en· W4297579214 on OpenAlexaff
John Lucas Massey, Mauricio Monzon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsArtificial liftLift (data mining)Gas liftDowntimeWinchEngineeringPetroleum engineeringDirectional drillingMarine engineeringGeologyMechanical engineeringDrillingComputer science

Abstract

fetched live from OpenAlex

Abstract This paper aims to share insights from a case history of jet lift applications in the Permian Yeso play. Apache Corporation has been actively drilling horizontally and multistage fracturing the Yeso formation in Eddy County, N.M., targeting dolostone/limestone/sandstone reservoirs interbedded with shale and anhydrite. The Yeso yields oil and liquids-rich gas at depths of 5,000-6,000 feet. Apache's initial strategy was to commence post-flowback production from fractured wells with electrical submersible pumps and then transition to rod lift as rates declined over time. However, as the wells approached the transition window between ESPs and rod pumps, high sand content, wellbore deviation and gas-to-liquids ratios caused frequent downtime for both types of lift, negatively impacting well performance. These conditions caused Apache to experiment with other forms of lift to seek a solution in horizontal wells in the transitionary window. This paper focuses on the trial and success of using concentric jet pumps in place of ESP and rod lift systems. Not all horizontal wells will be ideal candidates for this form of lift. This is especially true when ESP or rod lift can lift the well without issue or with limited downtime due to efficiency differences. However, jet lift is an underutilized form of lift that can produce wells from early life, through the steep horizontal decline, and into the late life steady state decline. This paper aims to show the versatility, under the right circumstances, inherent to downhole jet pumps with an example of a successful installation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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