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Record W2901039658 · doi:10.2118/193362-ms

SAGD Production Casing Failure Diagnosis and Repair

2018· article· en· W2901039658 on OpenAlexafffundabout
Bill Plaxton, Trent Pehlke, Dennis Baxter, Matt Crockett, T. M. V. Kaiser

Bibliographic record

VenueSPE Thermal Well Integrity and Design Symposium · 2018
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsSuncor Energy (Canada)
FundersSustainable Development Technology CanadaSuncor Energy Incorporated
KeywordsCasingCalipersRoot causeLoggingFailure mode and effects analysisOil fieldPetroleum engineeringEngineeringForensic engineeringGeologyComputer scienceReliability engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The operator experienced an unusual casing failure at a producing SAGD (steam assisted gravity drainage) oil well in summer of 2017. The subject well in the Firebag SAGD field of NE Alberta, Canada had operated successfully for over 11 years. Once the problem was identified, the well was shut in to determine the nature of the failure and options for repair and recovery so it could be returned to operation as soon as possible. Tasks included identifying and isolating the failure, establishing the cause and nature of the failure, and determining viable repair options. Logging diagnostics to measure/image the failure were performed, which included new ultra-sonic logging imaging technology, high-resolution multi-finger caliper logging, a downhole camera run and conventional eddy flux casing inspection log. Historical log data was also reviewed to assess whether the failure evolved over time, or if the mechanism was acute. Once the nature of the failure was established, the optimal repair method was chosen, planned and carried out. Sophisticated analysis of multi-finger caliper log data, camera images and new technology in the form of an ultrasonic imaging tool for the casing were utilized and are presented. A discussion of potential root cause mechanisms for thermal wells is provided, including a variety of failure modes that could be ruled out. Confidence in the failure mode specific to this well was increased by considering information acquired from multiple diagnostic tools. The nature of the connection failure determined from this process is outlined, along the rationale behind the repair method selected to remediate the well.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.655

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 head0.019
GPT teacher head0.229
Teacher spread0.210 · 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 designBench or experimental
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

Citations6
Published2018
Admission routes3
Has abstractyes

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