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Record W4244575082 · doi:10.2118/158409-ms

Simulation of Formation Damage after Long Term Water Flooding

2012· article· en· W4244575082 on OpenAlexaff
He Liu, Guoxin Li, Yiliang Li

Bibliographic record

VenueSPE Asia Pacific Oil and Gas Conference and Exhibition · 2012
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsPetro-Canada
FundersPetroChina Company Limited
KeywordsWater floodingPetroleum engineeringPermeability (electromagnetism)Flooding (psychology)Water injection (oil production)Formation waterGeotechnical engineeringPore water pressureWellboreEnvironmental scienceEnhanced oil recoveryGeologyChemistry

Abstract

fetched live from OpenAlex

Abstract Water flooding is a commonly used technology for enhancing oil recovery. Its mechanism is to maintain higher pressure in reservoir and to sweep the oil from the reservoir and push it towards production wells. However the strong water flooding will cause higher compression pressure around the injection wellbore. This high pressure in the reservoir causes stress redistribution and higher stress near the wellbore which induces material damage and permeability change. We developed a fluid-solid coupling finite element model to simulate and quantitatively analyze the pressure evolution in the reservoir as well as damage and permeability change in the formation during long term water flooding process. The obtained results offer theoretical understanding on the benefits (pore pressure increase in the simulation domain), rock damage, permeability change of long term water flooding and the insights of how to detect and prevent wellbore failure and collapse due to water flooding.

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)0.001
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.216
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 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

Citations2
Published2012
Admission routes1
Has abstractyes

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