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Record W2329850953 · doi:10.1061/9780784413388.070

Numerical Study of High Pressure Injection in Unconsolidated Reservoirs

2014· article· en· W2329850953 on OpenAlexaff
K. Atefi Monfared, L. Rothenburg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWater injection (oil production)Injection wellHydraulic fracturingGeologyPetroleum engineeringGeotechnical engineeringWellboreTransient analysisTransient (computer programming)Transient flowPore water pressureRotational symmetryStress (linguistics)Computer simulationMechanicsFlow (mathematics)Transient responseEngineeringSurge

Abstract

fetched live from OpenAlex

Numerous hydrocarbon-related injection operations are carried out in weakly-consolidated reservoirs. Strong flow-stress coupling, large non-linear deformations, and fracturing/opening are typical during these operations, specifically surrounding the injection wellbore. Geomechanical processes involved in high pressure injection in weakly-consolidated media are not well understood. The objective of this paper is to study the coupled behavior of these reservoirs at injection pressures below hydraulic fracturing, but sufficiently high to induce a zone of plastic deformations around the wellbore. A coupled, axisymmetric, poro-elasto-plastic, numerical model was developed in this study to simulate injection in unconsolidated formations. A detailed stress-strain-pore pressure evaluation was performed throughout the transient period of injection cycle, transient period of post-injection, and at steady state. Pressures were compared against the theoretical equation commonly applied at field and in analytical/numerical studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.218
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes1
Has abstractyes

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