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Record W2060271203 · doi:10.2118/157921-ms

Challenges While Performing Infill Cementing Amongst Existing SAGD Wells

2012· article· en· W2060271203 on OpenAlexaboutno aff
Jason Schneider, Chuck Sylvestre

Bibliographic record

VenueSPE Heavy Oil Conference Canada · 2012
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsInfillDrillingPetroleum engineeringWellboreLost circulationCompletion (oil and gas wells)Drilling fluidOil wellGeologyEngineeringCivil engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The drilling and completing of Infill wells (wells drilled amongst existing SAGD wells) is becoming common practice with many operators in North Eastern Alberta. These Infill wells pose numerous challenges that may not exist in the drilling and completing of a conventional SAGD well. In addition to conventional challenges such as lost circulation that need to be safely managed to have a successful completion strategy, the Infill wellbore has elevated temperatures and heat transfer rates due to contact with or close proximity to a steam chamber. There is no single, universal strategy used by operators to address and control these challenges, which require robust cementing solutions. Many clients are completing Infill cementing with expected bottom hole circulating temperatures (BHCT) ranging from 35°C to 100°C. This variation in clients’ BHCT results is due in part to the drilling, and more specifically, the circulation strategy used. Static periods of only a few hours can bring the effective temperature to that of the reservoir. As a conventional SADG strategy cannot be used for all wells, a specific blend design utilizing different technologies must be investigated. The large variations in bottom hole static temperature (BHST) and BHCT require complete understanding of the cement blend performance and its specific application. Cement blend designs that are applicable and can perform over a range of temperatures reduce the impact of these challenges. With the frequency of Infill cementing expected to increase, a greater understanding of the effective wellbore temperatures and the subsequent effective applicable temperature range of cement blends is needed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score1.000

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 head0.047
GPT teacher head0.215
Teacher spread0.168 · 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.

Study designOther design
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
Published2012
Admission routes1
Has abstractyes

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