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Record W2922464765 · doi:10.2118/185009-pa

Quantification of Sand Production Using a Pressure-Gradient-Based Sand-Failure Criterion

2019· article· en· W2922464765 on OpenAlexafffund
Zhaoqi Fan, Daoyong Yang, Xiaoli Li

Bibliographic record

VenueSPE Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCementation (geology)GeologyGeotechnical engineeringPetroleum engineeringPressure gradientScale (ratio)FluidizationFlow (mathematics)Oil productionEnvironmental scienceFluidized bedMaterials scienceMechanicsEngineeringCementComposite material

Abstract

fetched live from OpenAlex

Summary Cold heavy-oil production with sand (CHOPS) has been one of the major recovery processes for developing unconsolidated heavy-oil reservoirs by taking advantage of sand production and foamy-oil flow. However, effective characterization and accurate prediction of sand production is still a challenge. In this work, a pressure-gradient-based sand-failure criterion is proposed for quantifying sand production and characterizing wormhole propagation. The proposed sand-failure criterion was initially developed at the pore-scale level, while a pseudointeraction force between two neighboring sand grains was proposed to implicitly represent the potential contributions of cementation and geomechanical stresses to the fluidization of sand. The criterion was then extended to a grid scale within a wormhole because the pressure gradient is constant at either a pore scale or a grid scale. With the bottomhole pressure being an input constraint, the proposed sand-failure criterion was validated with good agreement by reproducing production profiles and wormhole propagation from laboratory experiments and a CHOPS well in the Cold Lake Oil Sands Area. This was a confirmation that the proposed sand-failure criterion can be used to characterize the sand production in a CHOPS process.

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 score0.028
Threshold uncertainty score0.313

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations29
Published2019
Admission routes2
Has abstractyes

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