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Record W2061982543 · doi:10.1134/s1028334x11030159

A new method for identification of protocatagenesis, mesocatagenesis, and apocatagenesis zones in a terrigenous sedimentary cover by applying layer seismic velocities

2011· article· en· W2061982543 on OpenAlexaboutno aff
D. V. Lazurkin

Bibliographic record

VenueDoklady Earth Sciences · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsGeologySedimentary rockTerrigenous sedimentSource rockGeochemistryDrillingHydrocarbon explorationStructural basinPetrologyGeomorphology

Abstract

fetched live from OpenAlex

This method pertains to oil and gas geology and to geology of sedimentary basins prospective for oil and gas. It includes identifying catagenesis zones in drilled areas within the sedimentary cover of the basin based on assay results for drill cores and cuttings using the standard methods. Analysis is primarily made in order to determine rock catagenesis based on the optical characteristics of vitrinite. A correlation between catagenesis zones and layer seismic velocities obtained from regional and exploration seismic data is made for a drilled area. Both the layer seismic velocities and the degree of rock catagenesis increase with depth under the influence of increasing rock density. Correlations between layer velocities and the degree of rock catagenesis have been established. The following ratios have been determined for the Scotian shelf, Canada, and the Barents shelf, Russia: a protocatagenesis zone (the cap) corresponds to layer seismic velocities (V lay) of 1.5–3.3 km/s, a mesocatagenesis zone (the principal hydrocarbon generation area) corresponds to V lay of 3.3–5.0 km/s, and an apocatagenesis zone (an area with a very low hydrocarbon potential) corresponds to V lay of over 5.0 km/s. An advantage of the new method of identification of catagenesis zones is that it can be used prior to drilling. Its conceptual originality and cost efficiency lie precisely in this.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.985

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 head0.051
GPT teacher head0.267
Teacher spread0.215 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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