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Record W4306411123 · doi:10.1021/acsomega.2c04497

Identifying Organic Matter (OM) Types and Characterizing OM Pores in the Wufeng–Longmaxi Shales

2022· article· en· W4306411123 on OpenAlexaff
Guoliang Xie, Weiduo Hao

Bibliographic record

VenueACS Omega · 2022
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of Anhui ProvinceTongling UniversityNational Natural Science Foundation of China
KeywordsOrganic matterGeologyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

adsorption experiments were performed to study the properties of pores for samples with different TOC and mineral compositions. The results show the following. (1) Pyrobitumen and kerogen can be distinguished under an optical microscope and SEM; the former can be further divided into pyrobitumen without a fixed shape and pyrobitumen with a certain shape, and the latter contains algal fragments, bacteria-like aggregates, graptolite, and micrinite. The overwhelming number of SEM-visible OM pores are mainly observed in pyrobitumen without a fixed shape, whereas pores in other OM types are complex. A PCAS analysis showed that meso-macropores are developed in pyrobitumen without a fixed shape, whereas pores in algal fragments and bacterial-like aggregates are mainly mesopores. (2) Quartz-rich brittle shale will provide more visible SEM pores compared to clay-rich ductile shale, and carbonates are unfavorable for pore development because they can block the pore as cements. Moreover, the rigid mineral framework, including that constructed by quartz recrystallization and pyrite cementation, and the pore-fluid pressure are favorable for the development of OM pores. (3) Adsorption experiments showed that pyrobitumen makes a great contribution to pore development, including micropores and meso-/macropores. Finally, we propose that the pore parameters (e.g., pore diameter, pore form factor, and deformation) of pyrobitumen without a fixed shape may characterize the enrichment condition of shale gas.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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 teacher head, 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

Citations7
Published2022
Admission routes1
Has abstractyes

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