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Record W2800463422 · doi:10.1093/gji/ggy167

Increased likelihood of induced seismicity in highly overpressured shale formations

2018· article· en· W2800463422 on OpenAlexafffundabout
David W. Eaton, Ryan Schultz

Bibliographic record

VenueGeophysical Journal International · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsAlberta EnergyUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOverpressureGeologyHydraulic fracturingPore water pressureKerogenOil shaleInduced seismicitySeismologySedimentary rockFluid pressureGeomechanicsPetrologySedimentary basinGeotechnical engineeringStructural basinSource rockGeochemistryGeomorphology

Abstract

fetched live from OpenAlex

SUMMARY Fluid-injection processes such as disposal of saltwater or hydraulic fracturing can induce earthquakes by increasing pore pressure and/or shear stress on faults. Natural processes, including transformation of organic material (kerogen) into hydrocarbon and cracking to produce gas, can similarly cause fluid overpressure. Here, we document two examples from the Western Canada Sedimentary Basin where earthquakes induced by hydraulic fracturing are strongly clustered within areas characterized by pore-pressure gradient in excess of 15 kPa m−1. Despite extensive hydraulic-fracturing activity associated with resource development, induced earthquakes are virtually absent in the Montney and Duvernay Formations elsewhere. Statistical analysis suggests a negligible probability that this spatial correlation developed by chance. This implies that, in addition to known factors such as anthropogenic pore-pressure increase and proximity to critically stressed faults, high in situ overpressure of shale formations may also represent a controlling factor for inducing earthquakes by hydraulic fracturing. On a geological timescale, natural pore-pressure generation may lead to fault-slip episodes that regulate the magnitude of formation overpressure.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

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)0.0010.000

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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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.

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

Citations101
Published2018
Admission routes3
Has abstractyes

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