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Record W2016996844 · doi:10.1190/1.1845142

Integration of geophysical methods with reservoir simulation

2004· article· en· W2016996844 on OpenAlexafffund
Ying Zou, L. R. Bentley, Laurence R. Lines

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsGeologySaturation (graph theory)Reservoir simulationAmplitudeOil productionSeismic inversionOil fieldSeismologyGeophysicsPetroleum engineeringMeteorology

Abstract

fetched live from OpenAlex

Time-lapse seismic modeling was conducted for the Pikes Peak heavy oil field using the results from a reservoir simulation model. Cyclical steam stimulation (CSS) started in 1981 and continues to the present. A flow simulation model was constructed for the region around a seismic profile that was acquired in 1991 and repeated in 2000. The simulator was run from the start of production in 1981 through 2000. The porosity, saturation, pressure and temperature were extracted from the reservoir zone from the flow simulator for the early-production condition in 1991 and almost 10 years later in 2000. The seismic response of the reservoir was computed using a fluid substitution procedure and seismic forward modeling. Comparing the results of 1991 and 2000 indicated that the gas saturation changes caused the largest change in the simulated seismic response. Seismic difference sections showed that thick zones of gas saturation caused more time delay in reflections below the producing zone by lowering velocity within the reservoir zone. Thin zones of gas caused reflection amplitude differences, but not much time delay differences. Temperature and pressure were also correlated with seismic changes, but not as quantitatively as the gas saturation. AVO analysis was used to map the gas distribution. The AVO mapped gas zones correspond reasonably well to the gas zones from reservoir simulation. The simulated seismic response difference section is similar in character to the observed difference sections.

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)0.001
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.305
Teacher spread0.276 · 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

Citations0
Published2004
Admission routes2
Has abstractyes

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