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Characteristics of Black Shale Reservoirs and Controlling Factors of Gas Adsorption in the Lower Cambrian Niutitang Formation in the Southern Yangtze Basin Margin, China

2017· article· en· W2688244601 on OpenAlexaff
Yuehao Ye, Chao Luo, Shugen Liu, Christopher Xiao, Bo Ran, Wei Sun, Di Yang, Luba Jansa

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of Canada
FundersNational Natural Science Foundation of China
KeywordsOil shaleQuartzMesoporous materialAdsorptionPorosityGeologyOrganic matterMineralogySpecific surface areaVolume (thermodynamics)Clay mineralsChemical engineeringGeochemistryChemistryGeotechnical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The pore structure and shale adsorption capacity have a great impact on the formation of shale gas field of yield industrial gas flow. This investigation focuses on the characteristics of the reservoir and adsorbed gas of the Lower Cambrian Niutitang Formation in the southern margin of the Yangtze basin. On the basis of geochemical analysis, low pressure nitrogen gas adsorption, X-ray diffraction, and isothermal adsorption experiments on core samples, the Niutitang Formation shows following characteristics: (1) The pore of the Niutitang shale can be divided into four categories: interparticles pores, intraparticles pores, organic matter pores, and microfractures. (2) The pores structure of shale shows three characteristics: the micropores frequency peaks are higher than that of mesopores in the d V (d) curve, and specific surface area frequency is greatest in micropores; these pore characteristics primarily appear in siliceous shale. Pore volume frequency is primarily dominant in mesopores, which mainly appear in carbonaceous shales. The pore volume peaks of micropores and mesopores are extremely low, and specific surface frequency is commonly dominant in mesopores, which mainly appear in silty shale. (3) Organic matter and quartz are beneficial to the growth of shale porosity, and organic matter in shale is the main control factor in the development of micropores, while quartz is a primary control factor in mesopore and macropore development. High clay mineral content is not conducive to shale porosity development and is particularly detrimental to the development of micropores. (4) The function relationship between buried depth and the adsorbed gas capacity was establish based on the relationship of temperature and pressure with gas adsorbed. The results suggest that the maximum gas adsorption capacity occurs at depths of around 1800 m, and pressure is the dominant control factor at depths below 1800 m, where gas adsorption capacity increases with depth, while temperature is the primary control factor at depths greater than 1800 m, where gas adsorption capacity decreases with depth. (5) Black shale of the Lower Cambrian under high evolution, Good pore structure (pore volume and specific surface area) and high total organic carbon are advantageous to the gas adsorption capacity of the highly mature Lower Cambrian black shales, while high maturity and clay content may inhibit gas adsorption capacity.

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 score0.086
Threshold uncertainty score0.171

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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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Citations23
Published2017
Admission routes1
Has abstractyes

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