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Record W2907144129 · doi:10.2118/194016-pa

Approximate Physics-Discrete Simulation of the Steam-Chamber Evolution in Steam-Assisted Gravity Drainage

2018· article· en· W2907144129 on OpenAlex
Enrique Gallardo, Clayton V. Deutsch

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSteam-assisted gravity drainagePetroleum engineeringSteam injectionCaprockPorous mediumOil sandsThermalEngineeringAsphaltEnvironmental sciencePorosityGeotechnical engineeringMaterials sciencePhysicsThermodynamics

Abstract

fetched live from OpenAlex

Summary Steam-assisted gravity drainage (SAGD) is a thermal-recovery process to produce bitumen from oil sands. In this technology, steam injected in the reservoir creates a constantly evolving steam chamber while heated bitumen drains to a production well. Understanding the geometry and the rate of growth of the steam chamber is necessary to manage an economically successful SAGD project. This work introduces an approximate physics-discrete simulator (APDS) to model the steam-chamber evolution. The algorithm is formulated and implemented using graph theory, simplified porous-media flow equations, heat-transfer concepts, and ideas from discrete simulation. The APDS predicts the steam-chamber evolution in heterogeneous reservoirs and is computationally efficient enough to be applied over multiple geostatistical realizations to support decisions in the presence of geological uncertainty. The APDS is expected to be useful for selecting well-pair locations and operational strategies, 4D-seismic integration in SAGD-reservoir characterization, and caprock-integrity assessment.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.392

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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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