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Record W2383979969

Evaluation of hydrocarbon accumulation conditions for shale gas from the Eastern Sag of the Liaohe Oilfield and its gas-bearing properties

2011· article· en· W2383979969 on OpenAlexaboutno aff
Nie Ling

Bibliographic record

VenueActa Petrologica Sinica · 2011
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil shaleGeologyMaturity (psychological)Source rockHydrocarbonGeochemistryNatural gasOrganic matterShale gasTotal organic carbonKerogenShell in situ conversion processPetroleum engineeringPetrologyStructural basinShale oilGeomorphologyEnvironmental chemistryPaleontologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

In comparison with the organic matter type,organic carbon abundance,thermal maturity and thickness of source rocks in shale-gas-bearing basins abroad,the present paper preliminarily confirmed the conditions for shale-gas accumulation in the Eastern Sag of the Liaohe Oilfield.The experimental study on gas amounts absorbed by shales indicated that there existed a positive correlation between the absorbed gas amount and the TOC and thermal maturity of dark mud shales in the Eastern Sag,and its adsorbed gas amount was 0.2~1.2 m3/t,basically equal to that of the Gordondale shale in Canada and the Lower Silurian Longmaxi Formation shale of the Sichuan Basin.A comprehensive evaluation on gas-bearing properties of shales in various formations was made by using abnormal values from the total hydrocarbon by gas logging and being combined with other criteria,such as TOC,Ro,thickness of the corresponding shales,therefore,the most favorable block for shale-gas exploration within the Eastern Sag was targeted for the Erjiegou-Jiazhangsi area.

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 score0.022
Threshold uncertainty score0.045

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 head0.159
GPT teacher head0.287
Teacher spread0.128 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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