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Fractal Characteristics of Lacustrine Tight Carbonate Nanoscale Reservoirs

2017· article· en· W2775034286 on OpenAlexaff
Qilu Xu, Yongsheng Ma, Bo Liu, Xinmin Song, Linkai Li, Jinze Xu, Jiao Su, Keliu Wu, Zhangxin Chen

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersChina Scholarship CouncilPetroChina Company LimitedMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsAuthigenicDiagenesisGeologyCarbonateTerrigenous sedimentMineralogyClay mineralsCalciteCarbonate mineralsCementation (geology)AragoniteGeochemistryChemistryMaterials scienceSedimentary rockCement

Abstract

fetched live from OpenAlex

The complexity and heterogeneity of pore structure greatly affect gas-liquid accumulation and transport, and the fractal theory has been proven to be an effective approach for studying nanoscale reservoirs in shale, coal, and tight sandstones. However, researches on fractal characteristics and control mechanisms for the lacustrine tight carbonate have received little attention. Lacustrine tight carbonate samples from the Jurassic Da’anzhai Member in the Sichuan Basin in China were systematically investigated focusing on the fractal characteristics and control mechanisms of storage spaces, minerals, diagenesis, and paleoenvironments. The fractal dimensions can be separated into two different and valid parts including D 1 (2.515–2.785, average 2.652) and D 2 (2.424–2.562, average 2.485), and the correlation between them is negative rather than positive. The average pore diameters exhibit a positive correlation with D 1 and a negative correlation with D 2, and the storage space is positively correlated with D 2 and negatively correlated with D 1 . Terrigenous minerals (e.g., quartz and clay) exhibit a positive correlation with D 2 and a negative correlation with D 1, whereas the effects of authigenic CaCO 3 minerals (e.g., calcite and aragonite) are exactly opposite to those of terrigenous minerals, which is due to the diagenesis and the own characteristics of minerals. CaCO 3 minerals can effectively change pore structures and fill the storage spaces (>5 nm) by cementation, compaction, pressure-solution, recrystallization, and replacement, whereas terrigenous minerals have developed irregular intraparticle pores, interparticle pores, and microcracks. The low salinity and the humid (rainy) paleoclimate are favorable for the formation of terrigenous minerals (elements), whereas they are harmful to the formation of authigenic minerals (elements), which increases D 2 and reduces D 1 . Additionally, paleoredox has a weak influence on the fractal dimensions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.223
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 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".

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Citations20
Published2017
Admission routes1
Has abstractyes

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