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Record W2536295433 · doi:10.2118/175036-pa

Steam-Assisted Gravity-Drainage Performance With Temperature-Dependent Properties—A Semianalytical Approach

2016· article· en· W2536295433 on OpenAlexaff
Mohammad Heidari, S. Hossein Hejazi, S.M. Farouq Ali

Bibliographic record

VenueSPE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSteam-assisted gravity drainageTransient (computer programming)ThermalPetroleum engineeringHeat transferThermal conductivityNonlinear systemMechanicsOil sandsGeologyComputer scienceMaterials scienceThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Summary Steam-assisted gravity drainage (SAGD) is one of the successful in-situ thermal-recovery methods for oil-sands production. In this paper, we provide a simple semianalytical model that can accurately analyze an SAGD project with variable properties. In particular, we investigate the effect of temperature-dependent properties such as thermal conductivity, heat capacity, and rock density on SAGD performance. The proposed model sequentially solves the transient nonlinear heat-transfer equation coupled with the continuity equation with Kirchhoff's transformation and the heat integral method (HIM). A criterion for timestep selection is defined on the basis of the Courant and Péclet numbers to guarantee the stability of the sequential technique. The results illustrate that the temperature-dependent physical properties affect temperature distributions ahead of steam chamber which consequently have a significant impact on the cumulative oil production and oil-production rate. Moreover, the results show that the temperature profile ahead of the steam chamber changes with time and space, and a 2D transient assumption for SAGD modeling is necessary. The semianalytical model runs in a small fraction of numerical-simulator runtime, yet it provides reasonable results. Thus, it has the potential to be used as a tool for quick SAGD evaluations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.548

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 integrity0.0000.001
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 head0.009
GPT teacher head0.189
Teacher spread0.180 · 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 designBench or experimental
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

Citations13
Published2016
Admission routes1
Has abstractyes

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