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Record W2230670913 · doi:10.2118/174403-ms

Practical Approach to Caprock Analysis

2015· article· en· W2230670913 on OpenAlexaff
M.R. Carlson, Michelle Uwiera, Peter B. Cooper, Dickson Lee, Adam MacDonald

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2015
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsCaprockDrillGeomechanicsPetroleum engineeringGeologyConstruction engineeringRisk analysis (engineering)Computer scienceEngineeringGeotechnical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The issues of caprock integrity have been thrust into the forefront of consideration and regulation. There have been several documented steam and bitumen leaks to surface and recently the AER has placed a moratorium on the licensing of shallow thermal in-situ oilsand projects. They have also sent out a series of draft requirements. In the interim, the draft letters are a good indicator of future requirements. A brief description will be made of the main technical issues in determining caprock integrity. A detailed discussion will also be made of the 5 separate documents issued by the AER. A practical approach to deal with these technical and regulatory requirements will be outlined. The foundation of all of this analysis is a good understanding of the conditions in the ground. The paper will concentrate on core acquisition, storage, analysis and preservation from the drill site to the laboratory. This is the starting point for laboratory testing, MOP determination, geomechanical modelling and thermal reservoir simulation. Failure to properly quantify the geomechanical properties and in-situ stress can increase the potential for failure. This could end a project, tie up capital, produce environmental liabilities and risk public safety. Responsible development of thermal projects requires that caprock integrity be quantified.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.249
Teacher spread0.206 · 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 designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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