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Record W2801228143 · doi:10.2118/174287-pa

Natural Fractures and Mechanical Properties in a Horn River Shale Core From Well Logs and Hardness Measurements

2018· article· en· W2801228143 on OpenAlexaffabout
Sheng Yang, Nicholas B. Harris, Tian Dong, Wei Wu, Zhangxin Chen

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsOil shaleBrittlenessGeologyLithologyHydraulic fracturingGeotechnical engineeringMineralogyPetrologyComposite materialMaterials science

Abstract

fetched live from OpenAlex

Summary This paper documents the formation of natural fractures in the Horn River Group, a major Canadian shale gas play, and addresses relationships between natural-fracture development and rock-mechanical properties derived from cores and well logs. Most natural fractures in the Horn River Shale are narrow vertical fractures, sealed with carbonate minerals. In this study, the formation of observed fractures is primarily determined by a lithology type, mineral composition, and rock-mechanical properties at the timing of fracturing. Brittleness is an important geomechanical property controlling the formation of fractures, because brittle shale is more easily fractured than ductile shale, and fractures in brittle shale tend to persist when the fracturing pressure is released. In this study, a hardness value measured by a commercial hardness tester is found to be a good proxy for the brittleness of shale layers. On the basis of a statistical analysis, the threshold values of both hardness and brittleness are estimated to predict the distribution of natural fractures, assuming that the mechanical properties of the host rock were relatively stable from at least the time at which fractures formed. Hardness values are shown to be more reliable than brittleness.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.046
GPT teacher head0.271
Teacher spread0.225 · 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 designSimulation or modeling
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

Citations17
Published2018
Admission routes2
Has abstractyes

Explore more

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