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Record W2560046058 · doi:10.2118/184145-ms

Well Design, Construction and Completion Considerations in a Thermal Oil Sand Development Project

2016· article· en· W2560046058 on OpenAlexaff
Mirko Zatka

Bibliographic record

VenueSPE Heavy Oil Conference and Exhibition · 2016
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsOperabilityCompletion (oil and gas wells)Process (computing)WellboreReliability (semiconductor)EngineeringPetroleum engineeringComputer scienceConstruction engineeringReliability engineering

Abstract

fetched live from OpenAlex

Abstract The sub-surface design of wellbores and associated well completions to be used in thermal oil sand operations must take into account a significant number of criteria and factors to ensure the delivered wells can provide the required long-term operating life and reliability required of them, especially when it comes to operating safety. These considerations cover a wide range of primarily technical aspects, including the location and layout of the wells at surface and sub-surface; type of service the wellbore will be used for, therefore, the construction materials to be used and their properties; recovery process impact on the wellbore design including the geology and reservoir behaviour; tubing size, perforation and sand control considerations; artificial lift requirements; downhole operation selectivity; produced fluid compositions and conditions, and well operability requirements. The level of detail that needs to be considered for each of these depends on the stage of project development. As work progresses, the level of detail increases in order to be able to arrive at an appropriate and balanced technical and safety design, as well as associated cost, once the final concept selection is made. The subsequent "Detailed Engineering" phase should be used only to fine-tune any remaining questions or issues that may arise in preparation for final project approval, and to provide the required definition of each element in order to be able to build / purchase / install / operate it. It is not discussed in this paper.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.035
GPT teacher head0.235
Teacher spread0.200 · 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
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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