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Record W2009410075 · doi:10.2118/65517-ms

Inflow Analysis and Optimization of Slotted Liners

2000· article· en· W2009410075 on OpenAlexaboutno aff
T. M. V. Kaiser, Stuart Wilson, L. A. Venning

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsInflowOutflowCharacterization (materials science)Marine engineeringPetroleum engineeringEnvironmental scienceMechanical engineeringComputer scienceGeologyEngineeringMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Abstract Slotted liners have been used for many years to provide sand control in oil industry applications. They are relatively inexpensive to manufacture and tolerant of installation loads, but historically they have not been able to offer the very small opening sizes of wire-wrap screens for controlling production of fine sands. However, recent advances in slot manufacturing methods provide slot openings that match and surpass the size and tolerance of wire-wrap screens. Furthermore, slotted liners offer an advantage in providing a variable slot density that can be used to optimize inflow or outflow distributions. In the development of its South Bolney reservoir, Marathon Oil Canada and Noetic Engineering Inc. performed an analytical evaluation of inflow characteristics for a new generation of commercially available slots. Several interesting conclusions were reached, the most significant being that inflow resistance depends much more strongly on slot density than on open area. The inflow characterization was also used to develop an optimized slot density distribution to promote more uniform production over the entire well. The slotting design was incorporated into a new well that was designed to control thermally induced loads, thereby avoiding the sand control characteristics of slotted liner being compromised.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.725
Threshold uncertainty score0.341

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 head0.002
GPT teacher head0.158
Teacher spread0.156 · 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

Citations19
Published2000
Admission routes1
Has abstractyes

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