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Record W2973241857 · doi:10.1177/0144598719876040

Effect of alkaline diagenesis on sandstone reservoir quality: Insights from the Lower Cretaceous Erlian Basin, China

2019· article· en· W2973241857 on OpenAlexaff
Shengyu Li, Jingchun Tian, Xiaobing Lin, Yinhui Zuo, Hua Kang, Dong-Dong Yang

Bibliographic record

VenueEnergy Exploration & Exploitation · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsAuthigenicDiagenesisGeologyIlliteChloriteGeochemistryQuartzDissolutionClay mineralsMineralogyPaleontologyChemistry

Abstract

fetched live from OpenAlex

The Tengger Formation in the Baiyinchagan sag of the Erlian Basin has behaved as a low-permeability petroleum system during its diagenetic history. Through the observation and examination of thin section, scanning electron microscopy, and X-ray diffraction data, this study found the existence of alkaline diagenesis, as indicated by the dissolution of quartz, the precipitation of authigenic illite and chlorite, and the formation of carbonate and authigenic albite cements. There were two types of alkaline diagenetic conditions: the early alkaline diagenetic conditions were controlled by the syndepositional environment, and the multiphase burial alkaline diagenetic conditions were controlled by the evolution of organic acids and thermal fluids. The quantitative model of the evolution of porosity over geologic time developed in this study indicates that quartz dissolution increased the porosity by a total of 0.85%. This work may provide significant advances in the understanding of the low-permeability reservoirs in the Erlian Basin and lays the scientific foundation for oil and gas exploitation.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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