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Record W4233346877 · doi:10.2523/84496-ms

Prediction of Sand Production Rate in Oil and Gas Reservoirs

2003· article· en· W4233346877 on OpenAlexaboutno aff
den P.J. van, M.B. Geilikman

Bibliographic record

VenueProceedings of SPE Annual Technical Conference and Exhibition · 2003
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringProduction (economics)Environmental scienceOil productionProduction rateFossil fuelGeologyProcess engineeringWaste managementEngineeringEconomics

Abstract

fetched live from OpenAlex

Most sand production prediction models to date have the capability to indicate whether initial sand production may take place somewehere during the lifetime of a reservoir. However, these models are unable to predict whether the sand production will be ‘problematic’ (e.g. in terms of erosion, plugging, well sand-up, separator fill, etc.), in particular for systems that have some tolerance towards sand production.In order to predict whether sand production will be ‘problematic’, one needs to be able to estimate sand production volumes and rates as a function of, amongst others, bottom-hole load conditions, drawdown, and time. This paper presents a model for the prediction of sand production volumes and rates for any type of clastic oil or gas reservoir. This work builds on successful previous efforts to predict sand production rate in the Canadian heavy oil sands. The current model is a validation of the previous model and its further generalisation to sandstones of any strength (not just unconsolidated) with any reservoir fluid (not just heavy oil), and involves features such as failure of intact sand, post-failure cavity stabilisation, and non-associated viscoplasticity. The model has been calibrated to laboratory experiments.

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 score0.026
Threshold uncertainty score0.375

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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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

Citations4
Published2003
Admission routes1
Has abstractyes

Explore more

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