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Record W1987045710 · doi:10.1190/1.3663403

Inferring in-situ stress changes by statistical analysis of microseismic event characteristics

2011· article· en· W1987045710 on OpenAlexaff
Mélanie Grob, Mirko van der Baan

Bibliographic record

VenueThe Leading Edge · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicroseismHypocenterGeologySeismologyFractal dimensionStress fieldGeomechanicsOverpressureFractal analysisInduced seismicityStress (linguistics)Slip (aerodynamics)FractalGeotechnical engineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

Two commonly estimated fractal dimensions, called b and D values, represent statistical characteristics in the distribution of magnitude sizes (b) and spatial hypocenter locations (D) of microseismic events, respectively. We establish that the values of these two dimensions are related to specific stress regimes. Through the case study of a heavy-oil field drained using cyclic steam stimulation, we infer that the measured temporal variation in fractal dimension b is most likely due to significant changes in the local stress regime over a seven-month period, ranging from extensional faulting (fractures opening), via a strike-slip regime, to finally compressive faulting (fractures closing). The fracture dimension D indicates predominantly planar-to-spherical hypocenter spatial distributions in the first and last stages, but changes to a more linear-to-planar spatial pattern in the intermediate strike-slip regime when the vertical stress is anticipated to be in between the maximum and minimum horizontal stresses. These changes could be due to localized pore-fluid overpressure. A statistical analysis of the microseismic event locations and their magnitudes is therefore a useful method to understand reservoir geomechanics and thereby facilitate its management.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.613

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 head0.033
GPT teacher head0.247
Teacher spread0.214 · 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

Citations29
Published2011
Admission routes1
Has abstractyes

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