MétaCan
Menu
Back to cohort
Record W3166254728 · doi:10.1155/2021/5517494

Numerical Simulation Study of Pore-Throat Evolution of Upper Paleozoic in Ordos Basin, China

2021· article· en· W3166254728 on OpenAlexaff
Qing Cao, Zhangxin Chen, Jingzhou Zhao, Jiacheng Dang, Jiaxuan Song, Chen Bin

Bibliographic record

VenueGeofluids · 2021
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersEducation Department of Shaanxi Province
KeywordsGeologyDiagenesisPorosityPermeability (electromagnetism)QuartzPetroleum reservoirStructural basinPaleozoicPetrologyCalciteGrain sizeMineralogyGeotechnical engineeringGeochemistryGeomorphologyPetroleum engineeringPaleontology

Abstract

fetched live from OpenAlex

The parameters of grain size, contents of silica, kaolinite, hydromica, calcite, and a geological time of tight sandstone reservoirs in Upper Paleozoic in Ordos basin were researched thoroughly, and the impact of the diagenetic evolution process of different sandstone types on porosity and throats was analyzed, based on the quantitative statistics from thin sections, measurements of porosity and permeability, and conventional and constant-rate mercury injection tests. We not only build the evolution of porosity through process-oriented numerical simulations during the geological time but also establish effect-oriented numerical simulations between porosity and different diagenesis parameters. Furthermore, we set up a fitting relationship between diagenetic factors and pore throats in different gas-bearing reservoirs. Differentiation results in the evolution of porosity and a pore-throat system of sandstone types have clear characteristics, such as lithic quartz sandstones of the He 8 Member in the Sulige area and quartz sandstones of the Shan 2 Member in the Yulin area. The fitting results show that the main factors influencing the evolution of porosity and a pore-throat system are grain size and siliceous cement, which can also be validated by the measured data on two gas-bearing intervals. The results are important to a deep understanding of the relationship between the reservoir continuing to experience porosity and permeability evolution and the timing of petroleum charging into the reservoir and can also be applied elsewhere as a quick means in high grading areas of risks during field development.

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 score0.112
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.009
GPT teacher head0.237
Teacher spread0.229 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGeofluidsSame topicHydrocarbon exploration and reservoir analysisFrench-language works237,207