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Relationship between Hydrocarbon Gas Generation and Kerogen Structural Evolution Revealed by Closed System Pyrolysis and Quantitative Py-GC Analysis of a Type II Kerogen

2020· article· en· W3116223619 on OpenAlexaff
Yijun Zheng, Chunqing Jiang, Yuhong Liao

Bibliographic record

VenueEnergy & Fuels · 2020
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of Canada
FundersState Key Laboratory of Organic GeochemistryMinistry of Science and Technology of the People's Republic of ChinaChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsKerogenHydrocarbonPyrolysisChemistryMaturity (psychological)Aromatic hydrocarbonGas chromatographyOrganic matterSource rockMineralogyAnalytical Chemistry (journal)Organic chemistryGeologyChromatography

Abstract

fetched live from OpenAlex

Kerogen can release both liquid and gaseous hydrocarbons during thermal maturation. In this study, the maturation and hydrocarbon generation of a low-maturity type II kerogen were simulated via temperature-programmed gold tube closed system (GTCS) pyrolysis at two different heating rates. In addition to the yields of C 1 –C 5 gaseous hydrocarbons, H/C atomic ratios and δ 13 C values were also obtained on the kerogen residues from GTCS pyrolyses. The remaining C 1 –C 5 gaseous hydrocarbon and C 6 + liquid hydrocarbon generation potentials in both the original kerogen and the kerogen residues of various thermal maturities were determined by quantitative flash pyrolysis-gas chromatography (Py-GC) and were used to estimate the loss of aliphatic carbons in artificially matured kerogens compared with that in the original kerogen. The yields of C 1 –C 5 gaseous hydrocarbons from GTCS pyrolysis were compared with the lost amount of C 1 –C 5 gaseous hydrocarbon potentials within the kerogen structure at the same maturities. The results indicate that the H/C atomic ratios of kerogen residues decreased and the δ 13 C values of kerogen residues became progressively heavier (i.e., being enriched in 13 C) with increasing maturity in the oil generation window (OGW) but with δ 13 C becoming slightly lighter above the calculated R o (Calcd R o, namely, Easy R o or equivalent vitrinite reflectance) of 2.5%. It is worth noting that, in the OGW, the yields of C 1 –C 5 gaseous hydrocarbons from GTCS pyrolyses were significantly lower than the amounts of lost C 1 –C 5 potentials indicated by Py-GC. We speculate that hydrocarbon gases generated in OGW may either be partially adsorbed within kerogen and dissolved in oil or participate in the thermal degradation of kerogen during GTCS pyrolyses, resulting in lower yields of C 1 –C 5 gaseous hydrocarbons than the decrease in kerogen C 1 –C 5 potentials. The Py-GC results of kerogen residues also show that the rates of loss for C 1, C 2, and C 3 hydrocarbons are about 92%, 98%, and 99%, respectively, at 1.3% Calcd R o . This indicates that most of the C 2 + alkyl side chains can be cleaved from the kerogen structure within OGW. In comparison, at a Calcd R o of 1.3%, the formation rate of methane (C 1 ) from GTCS pyrolysis of kerogen at a heating rate of 2 °C/h only amounted to about 32%. The results from this study appear to suggest that, in GTCS pyrolysis, most C 1 generated from type II kerogen at the overmature stage cannot be attributed to primary kerogen cracking but to gradual secondary cracking of hydrocarbons with high molecular weights.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.026
GPT teacher head0.238
Teacher spread0.212 · 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 designBench or experimental
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

Citations8
Published2020
Admission routes1
Has abstractyes

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