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Record W2551770452 · doi:10.1038/ncomms13614

Secondary migration and leakage of methane from a major tight-gas system

2016· article· en· W2551770452 on OpenAlexaffabout
James M. Wood, Hamed Sanei

Bibliographic record

VenueNature Communications · 2016
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of CanadaEncana (Canada)
Fundersnot available
KeywordsTight gasMethaneNatural gasGeologyHydrocarbonOil shaleSedimentary rockOverburdenPermeability (electromagnetism)Source rockCarbon dioxideShale gasPetrologyPetroleum engineeringHydraulic fracturingGeochemistryStructural basinGeomorphologyGeotechnical engineeringChemistryPaleontology

Abstract

fetched live from OpenAlex

Tight-gas and shale-gas systems can undergo significant depressurization during basin uplift and erosion of overburden due primarily to the natural leakage of hydrocarbon fluids. To date, geologic factors governing hydrocarbon leakage from such systems are poorly documented and understood. Here we show, in a study of produced natural gas from 1,907 petroleum wells drilled into a Triassic tight-gas system in western Canada, that hydrocarbon fluid loss is focused along distinct curvilinear pathways controlled by stratigraphic trends with superior matrix permeability and likely also structural trends with enhanced fracture permeability. Natural gas along these pathways is preferentially enriched in methane because of selective secondary migration and phase separation processes. The leakage and secondary migration of thermogenic methane to surficial strata is part of an ongoing carbon cycle in which organic carbon in the deep sedimentary basin transforms into methane, and ultimately reaches the near-surface groundwater and atmosphere.

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 score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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

Citations70
Published2016
Admission routes2
Has abstractyes

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