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Record W7097893631

P-PandP-SVseismicdatafromLousana Analysis of P-P and P-SV seismic data from Lousana,

2014· article· en· W7097893631 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSeismic to simulationOffset (computer science)Synthetic dataSeismic inversionData qualityLithologyOoidBoreholeReservoir modelingNorth sea
DOInot available

Abstract

fetched live from OpenAlex

Two orthogonal three-component seismic lines were shot by Unocal Canada Ltd. in January, 1987, over the Nisku Lousana Field in central Alberta. The purpose of the survey was to investigate a Nisku patch reef thought to be separated from the Nisku shelf to the east by an anhydrite basin. These data have been reprocessed and are currently being analyzed and used to develop methods for multicomponent seismic data analysis. The data are of overall good quality and major events were confidently correlated between the P-P and P-SV sections using offset synthetic seismograms. P-SV offset synthetic models were also used to extract interval Vp/Vs values from the P-SV data. Forward log-based offset P-P and P-SV modelling shows character, isochron, and Vp/Vs variations associated with lithology and porosity changes between reservoir and non-reservoir rock in the Nisku. The modelling results indicate that multicomponent seismic analysis is applicable in carbonate settings, even when the reservoir intervals are relatively thin (23 m). To date, we have had difficulty applying the modelling results to this data set because of multiple contamination in the zone of interest and work is ongoing to resolve this issue. Full-waveform modelling is planned to better understand the multiple activity on both components. However, there are observable anomalies on the data which are currently under investigation. Multicomponent recording provides additional seismic measurements of the subsurface to assist in developing an accurate geological model. Rock properties which can be extracted from elastic-wave data, such as Vp/Vs, reduce the uncertainty in predictions about mineralogy, porosity, and reservoir fluid type. Joint interpretation of P-P and P-SV data was helpful in horizon picking and in providing feedback on the validity of the interpretation. The interpretation techniques described in this paper can be usefully applied to other areas, including other carbonate plays.

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)0.000
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 score0.274
Threshold uncertainty score0.544

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.029
GPT teacher head0.242
Teacher spread0.213 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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