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Record W2041005825 · doi:10.2118/112161-ms

Subsea Production Enhancement—Success and Failure Over Five Years

2008· article· en· W2041005825 on OpenAlexfundno aff
Eamonn McGennis

Bibliographic record

VenueAll Days · 2008
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
FundersAGE-WELL
KeywordsSubseaContext (archaeology)Work (physics)Completion (oil and gas wells)Petroleum engineeringProduction (economics)Natural gas fieldIntervention (counseling)PerforationEngineeringMarine engineeringComputer scienceGeologyMechanical engineeringNatural gasEconomics

Abstract

fetched live from OpenAlex

Abstract Subsea well intervention has been around in the North Sea for many years as documented in papers such as Pollock (1990) but in the last five years there has been renewed interst due to a combination of high oil price and aging wells. This paper will look at the actual results achieved with production enhancement work done in subsea wells across the United Kingdom (UK) sector of the North Sea over a five year period 2002 to 2007. This time frame allows the results to be seen in the context of longer term field value. All the work included in the paper was done rig-less with a specialist subsea well intervention vessel. It covers a range of different oil producing fields operated by a number of different companies. The work done was through tubing and included zone isolation and re-perforation. No work was done in any gas fields. The paper will detail the production profiles of the wells both before and after the intervention work along with a discussion of the work done and an explanation of the factors that influenced the final result both good and bad. To facilitate an open discussion of the success and failures all data is presented anonymously. The high cost of subsea wells makes it essential to maximise the overall recovery per subsea well. This paper will detail actual results which may point the way forward.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.348

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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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 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

Citations3
Published2008
Admission routes1
Has abstractyes

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