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Record W2087313752 · doi:10.2118/149371-pa

Design of Caprock Integrity in Thermal Stimulation of Shallow Oil-Sands Reservoirs

2013· article· en· W2087313752 on OpenAlexafffund
Yanguang Yuan, Bin Xu, C. Palmgren

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsBitCan (Canada)
FundersAlberta Innovates
KeywordsCaprockPetroleum engineeringGeologyDeformation (meteorology)GeomechanicsOil sandsGeotechnical engineeringAsphaltMaterials science

Abstract

fetched live from OpenAlex

Summary Stakeholders in in-situ oil-sands development take caprock-integrity issues seriously. The industry is faced with the challenge of determining an optimal operating pressure in the reservoir where, in general, the pressure should stay significantly low to ensure the caprock integrity while being significantly high for enhanced oil production and economics. This paper presents a comprehensive work program on the subject for a shallow oil-sands play. Caprock integrity considers the induced stress and deformation in a caprock during the thermal stimulation of an oil-sands reservoir. A minifrac-test program is undertaken to define the original in-situ stress state. Laboratory tests are carried out to measure the deformation and strength properties. Simulations are run to calculate the induced stresses and evaluate them against the mechanical strength. This paper describes some important quality-control issues for these activities. For the minifrac tests, multiple cycles and use of flowback are promoted for enhanced efficiency and accuracy. Laboratory tests are recommended on whole cores in a drained condition at a slow strain rate. Numerical simulations should use site-specific and laboratory-measured material properties. On the basis of the limited sensitivity analyses, the thermal expansion coefficient of the reservoir and Young's modulus of the caprock are found to significantly affect the caprock deformation and/or induced stresses.

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: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations27
Published2013
Admission routes2
Has abstractyes

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