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Record W2021873472 · doi:10.1144/gsl.sp.2007.270.01.17

Characterizing fracture and matrix heterogeneities in folded Devonian carbonate thrust sheets, Waterton tight gas fields, Western Canada

2007· article· en· W2021873472 on OpenAlexaffabout
Keith Rawnsley, Martin de Keijzer, Lingli Wei, Solenn Bettembourg, W. Asyee, J.L. Massaferro, P. Swaby, Donna Drysdale, D. J. Boettcher

Bibliographic record

VenueGeological Society London Special Publications · 2007
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsDevonianCarbonateTight gasGeologyThrustMatrix (chemical analysis)Carbonate rockPetrologyFracture (geology)PaleontologyPhysicsMaterials scienceComposite materialHydraulic fracturingSedimentary rock

Abstract

fetched live from OpenAlex

Abstract Well performance is extremely variable in the stacked sequence of relatively tight Devonian and Mississippian carbonates in the northern part of the Waterton Complex, Alberta, Canada. This is despite having an extensively developed fracture system present in all the wells. In order to determine why some wells penetrated more permeable fractures than others, a full reinterpretation of the geophysical, structural, stress, matrix and dynamic data sets was carried out. Flow simulations at sector scales using discrete fracture network models and fullfield continuum modelling were used to test a range of geological and dynamic scenarios. One of the most northwestern fields of the Waterton complex, the West Carbondale field, is the focus of the work presented. For this field the best-fit dynamic models consist of a major fracture zone, corresponding to either a seismic scale lineament or zone of enhanced curvature, trending through the area of most prolific wells. Outside this zone, the vast majority of the fracture system makes little contribution to the flow in the wells, other than slightly enhancing the reservoir permeability.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.995

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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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

Citations27
Published2007
Admission routes2
Has abstractyes

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