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Record W2152401593 · doi:10.1190/1.2978975

Seismic rock physics of steam injection in bituminous oil reservoirs

2008· article· en· W2152401593 on OpenAlexafffundabout
Evan Bianco, Sam T. Kaplan, Douglas R. Schmitt

Bibliographic record

VenueThe Leading Edge · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPetroleum engineeringSteam injectionAsphaltOil sandsGeologyEnvironmental scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This case study explores rock physics properties of heavy oil reservoirs subject to the steam-assisted gravity drainage (SAGD) thermal enhanced recovery process (Butler, 1998; Butler and Stephens, 1981). Previous measurements —e.g., Wang et al. (1990) and Eastwood (1993)—of the temperature-dependant properties of heavy oil-saturated sands are extended by fluid-substitution modeling and wireline data to assess the effects of pore fluid composition, and pressure and temperature changes on the seismic velocities of unconsolidated sands. Rock physics modeling is applied to a typical shallow McMurray Formation reservoir (depth of 135–160 m) within the bituminous Athabasca oil-sands deposit in western Canada to construct a rock-physics-based velocity model of the SAGD process. Although the injected steam pressure and temperature control the fluid bulk moduli within the pore space, the effective stress-dependant elastic frame moduli are the most poorly known, yet most important, factors governing the changes of seismic properties during this recovery operation. The results of the fluid substitution are used to construct a 2D synthetic seismic section to establish seismic attributes for analysis and interpretation of the physical SAGD process. The findings of this modeling promote a more complete description of 11 high-resolution, time-lapse 2D profiles over some of the earliest steam 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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.733

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)0.0000.000

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 head0.031
GPT teacher head0.230
Teacher spread0.199 · 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 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

Citations17
Published2008
Admission routes3
Has abstractyes

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