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Record W1704487889

Modeling and mapping the effects of heat and pressure outside a SAGD steam chamber using time-lapse multicomponent seismic data, Athabasca oil sands, Alberta

2013· article· en· W1704487889 on OpenAlexaboutno aff
Loren Michelle Zeigler

Bibliographic record

VenueDigital Collections of Colorado (Colorado State University) · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersColorado School of Mines
KeywordsOil sandsPetroleum engineeringGeologySteam injectionEnvironmental scienceAsphaltMaterials science
DOInot available

Abstract

fetched live from OpenAlex

The field of study is a bitumen producing reservoir within the McMurray Formation.The deposit is a part of the Athabasca oil sands trend in Northeastern Alberta, Canada.This field contains 16 well pads that are, combined, producing more than 41,000 BOPD.Bitumen reservoirs are unique as a result of their high viscosity, low API gravity oil.This oil in this field has been produced by means of a method called Steam Assisted Gravity Drainage (SAGD), since 2007.In this method, two vertically stacked, horizontal wells are drilled.The upper well injects high temperature, high pressure steam and as the viscosity of the bitumen decreases it will begin to flow, via gravity, down to the lower producing well.Reservoir monitoring in this field is very important for multiple reasons, including the shallow depth and the large velocity changes that result from SAGD production.In order to map these changes, time-lapse multicomponent data were incorporated with rock physics modeling in order to map and interpret changes in Vp/Vs with production.When fluid substitution results and pressure estimations are combined, the resulting velocities are consistent with the core sample modeling done by Kato et al. (2008).These results were then compared with the seismic data in order to identify areas affected by steam, heat, and pressure within the reservoir through time-lapse Vp/Vs.PP time-lapse results show the location of the steam chamber within the reservoir, however these data do not give any information about the effects of pressure or heat.Converted-wave (PS) data can be used to image pressure and viscosity changes in the reservoir.When these data are combined into a Vp/Vs volume, the effects of steam, heat and pressure can be identified.Vp/Vs areas of little to no difference indicate steamed zones while the surrounding areas with large differences indicate heated and pressured zones.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.692

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.016
GPT teacher head0.200
Teacher spread0.185 · 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 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

Citations1
Published2013
Admission routes1
Has abstractyes

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