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Record W2746712503 · doi:10.1190/segam2017-17661198.1

The application of seismic-derived rock properties in predicting Duvernay-induced fractures

2017· article· en· W2746712503 on OpenAlexaffabout
Ronald Weir, David W. Eaton, Laurence R. Lines, Don C. Lawton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeologyReefDrillingDevonianMicroseismStructural basinOil shaleSource rockSedimentary basinCarbonateGeochemistryHydraulic fracturingSedimentary rockPaleontologySeismologyOceanography

Abstract

fetched live from OpenAlex

The Duvernay shale, equivalent to the Devonian age Muskwa member of the Horn River Group, is a major resource play in the Western Canadian Sedimentary Basin. The Duvernay shale is rich in organic matter and, depending upon situation within the basin, produces gas, natural gas liquids, or oil. It is commonly believed to be the source rock for the Leduc reef, Nisku, and Wabamun carbonate plays. With the development of horizontal drilling, and multi-stage hydraulic fracturing, the Duvernay is a desirable exploration target, especially within the condensate window. The rock type varies in accordance with its position in the depositional basin; near Leduc reef buildups, there is reef debris, whereas away from the reef it is deposited in a carbonate bank environment. Vertical wells and cores are evaluated for rock properties, total organic hydrocarbon content, and suitability for further development. Microseismic surveys are routinely carried out for Duvernay well completions to monitor the induced fractures and seismicity. There is often a discrepancy between the homogeneous reservoir assumption, and the observed microseismic events. Prestack seismic inversions can provide valuable information about reservoir characterization and fractures. Prestack seismic attributes such as Poisson’s Ratio, Young’s Modulus, brittleness, P and S impedances can be extracted from inverted seismic data. AVO inversion may provide information about density and orientation of natural fracture network. A common assumption made in horizontal drilling programs is that the lithology is homogeneous, and that fracture orientations are determined by the direction of maximum horizontal stress. This is not always the case, as observed in microseismic observations following a completion program. Lithology and preexisting fractures play a significant role in determining the size and patterns of the newly induced fractures. Seismic attributes can be combined with geological mapping, and a combined reservoir characterization map can be produced. Meaningful facies relationships can be established between seismic attributes, reservoir quality, and lithofacies. Seismic reservoir characterization can give relationships between rock properties, hydraulic fracture performance, and hydrocarbon production. The seismically mapped brittleness variations may explain why observed micro seismicity departs from the homogeneous lithology assumption. Presentation Date: Tuesday, September 26, 2017 Start Time: 3:05 PM Location: 370D Presentation Type: ORAL

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.240
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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
Published2017
Admission routes2
Has abstractyes

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