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Record W2059781670 · doi:10.3997/2214-4609.20140720

Seismic Efficiency and Overshoot of Fractures Associated with Stimulation in Heavy Oil Reservoirs

2014· article· en· W2059781670 on OpenAlexaff
Lindsey Nicole Meighan, T. Urbancic, A. M. Baig

Bibliographic record

VenueProceedings · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsCanadian Apheresis Group
Fundersnot available
KeywordsOvershoot (microwave communication)MicroseismFlooding (psychology)Fault (geology)GeologyWater floodingEnvironmental scienceRange (aeronautics)Petroleum engineeringSeismologyMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Summary To better understand the different fracturing process of two passive seismic datasets collected over a 30 month period for two heavy oil reservoirs, utilizing steam injection and water flooding, we investigate radiated energy and seismic efficiency. We calculate the Savage-Wood Efficiency (ŋsw) and overshoot of microseismic events recorded in two reservoirs with different stimulations and find under similar geological conditions and time period, the seismic efficiency, for steam injection compared to water flooding are unique across the datasets implying that temperature and fluid pressure has a different impact on the fracturing dynamics. Reservoir A (stream injection), has 4069 events (Mw = −2.2 top −0.2) with low efficiency (ŋsw=0.01 to 0.48) and considered in overshoot (ε=0.1 to 0.5). Reservoir B (water flooding), has, 1763 events, but higher magnitude range (Mw = −1.4 to 1.8); with a combination of low efficiency (overshoot ε=0.002 to 0.49) and high efficiency (undershoot ε=−415 to 0.49) events. Overshoot occurs when the dynamic strength of the fault is relatively higher than the final stress acting on the fault and less energy is radiated; where undershoot occurs when the dynamic strength is relatively lower than the final stress on the fault and in a state of dynamic weakening.

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 score0.494
Threshold uncertainty score0.350

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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