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Record W2033312088 · doi:10.3997/2214-4609.20142164

Infering Dynamic Rock Properties During Hydraulic Fracturing from Microseismicity

2014· article· en· W2033312088 on OpenAlexaff
Lindsay Smith-Boughner, A. M. Baig, T. Urbancic

Bibliographic record

VenueProceedings · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsCanadian Apheresis Group
Fundersnot available
KeywordsMicroseismHydraulic fracturingGeologyShear (geology)AnisotropySeismologyFracture (geology)Passive seismicOil shalePetrologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Summary Being able to estimate the dynamic properties of rock failure in shale from microseismic data allows for the characterization of reservoir rock at resolutions that are unattainable from conventional 3D seismic imaging and allows for additional insight into the fracturing process that will help constrain different models for the fracture generation. Using events generated by hydraulic fracturing, we demonstrate a technique for estimating the Vp/Vs ratios throughout a hydraulic fracture from seismic moment tensors. Downhole recording microseismic events allow us to estimate the seismic moment tensor of shear-tensile events. Using a set of well-located, high quality events with similarly oriented fracture planes, we can invert for Vp/Vs, the ratio of the compressional to shear wave velocities, of the volume around the fracture. While traditionally this parameter is used as a proxy for reservoir rock properties, decreases in this ratio likely indicates a reduction in the rock strength due to fracturing. From sets of events on nearly vertical fractures, we observe decreasing Vp/Vs ratios throughout the stage. Lowering Vp/Vs is also observed in previously fractured areas. We also estimate the variations in apparent Vp/Vs as a function of the dip of the fracture plane to constrain anisotropic responses.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.649

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 communication0.0000.001
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 head0.008
GPT teacher head0.180
Teacher spread0.172 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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