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Record W4401460558 · doi:10.1115/omae2024-126740

Characterization of Dynamic Pore-Throat Structure and Petrophysical Properties in an Unconsolidated Sandstone Reservoir

2024· article· en· W4401460558 on OpenAlexaff
Lizhen Ge, Guangfeng Liu, Zhoujun Luo, Teng Ma, Daoyong Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPetrophysicsGeologyCharacterization (materials science)Reservoir modelingPetrologyThroatPetroleum engineeringMineralogyGeotechnical engineeringPorosityMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Abstract In this study, a comprehensive and practical technique was developed to study the dynamic changes of pore-throat structure and petrophysical properties of unconsolidated sandstone reservoirs during long-term water injection. Experimentally, core samples were gathered from unconsolidated sandstone reservoirs for thin-section preparation and analyzed using laser particle size and X-ray diffraction techniques. In addition to identifying and classifying pore types and clay minerals, the size, fraction, and distribution of particles were also determined and categorized. Furthermore, a high-pressure mercury injection test was conducted on the core after waterflooding so as to quantify alterations in the microscopic pore-throat structure. The dispersion of clay minerals breaks the formation bond, leading to the fall and migration of clay and fine silt, and the content of clay and fine silt decreases, resulting in the change of pore-throat structure. Compared with the change of pores, the change of throat has a greater influence on the seepage behaviour, but such a change mainly occurs in the early stage of waterflooding. The fractal dimension of the pore-throat structures increases from 2.51 to 2.58, enhancing the heterogeneity and making the seepage channels more concentrated. The pore-throat structure was changed by 90% when water was injected to 200 PVs, gradually changed until 300 PVs of injected water, and then remained basically unchanged afterwards.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.216
Teacher spread0.209 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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