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Record W2043232278 · doi:10.2118/114687-ms

Cost Evaluation for the First Running Experience of Expandable Openhole Liner for Unexpected Problems in Drilling Well, Iran

2008· article· en· W2043232278 on OpenAlexaff
Y. Yousafi, Mohammad Fazaelizadeh, G. Hareland, A. Kustamsi, S. A. Mirhaj, Farid Shirkavand

Bibliographic record

VenueIADC/SPE Asia Pacific Drilling Technology Conference and Exhibition · 2008
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBoreholePetroleum engineeringDrillingWellboreLost circulationCompletion (oil and gas wells)GeologyWell drillingDrilling fluidMarine engineeringEngineeringGeotechnical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The expandable openhole liner system is used as solution for operational challenges associated with borehole instabilities, pore pressure/fracture gradient issues and the effect of salt or subsalt formation. All these operational challenges produce pre- mature downsizing of well's tubular. While drilling in the Kupal oilfield located in south west of Ahwaz, Iran, an isolated extreme thief zone was penetrated resulting in a possible unplanned conventional liner was run, decreasing further deeper hole sizes. The smaller size of liner would have cause problems such as lower reservoir production rate and putting limitations on running measurement tools to evaluate the reservoir. Because of these aforementioned negative effects of running a conventional liner it was instead desided to run an expandable openhole liner. This paper discusses challenges and solutions for running the first expandable openhole liner in Iran to reach a proposed target with the planned hole sizes as well as a cost evaluation and comparison of two wells, one using expandable open hole liner and one using conventional liner.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.050
GPT teacher head0.261
Teacher spread0.211 · 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 designNot applicable
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

Citations1
Published2008
Admission routes1
Has abstractyes

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