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Record W2398690022 · doi:10.2118/180712-ms

Understanding Impacts of Lean Zones on Thermal Recovery in View of Mobile Water

2016· article· en· W2398690022 on OpenAlexaffabout
Jinze Xu, Yi Pan, Zhangxin Chen

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSaturation (graph theory)Petroleum engineeringSteam injectionWater saturationEnvironmental scienceThermalEnhanced oil recoveryGeologyPorosityGeotechnical engineeringGeography

Abstract

fetched live from OpenAlex

Abstract Lean zones have been reported in Nexen's Long Lake project and Suncor's Firebag project in Canada. With high steam-oil ratio and low oil recovery caused by lean zones, developing a heterogeneous oil sand reservoir is a considerable challenge. To study the mechanism of how lean zones affect thermal recovery performance, the role of the huge volume of mobile water cannot be neglected. In this paper, we propose a new mass transfer model for the thermal recovery process in oil sands with mobile water. Further discussions based on the model showed the following conclusions: (1) Reservoir regions during thermal recovery are divided into four regions based on initial water saturation. An increase in the initial water saturation may cause the oil bank to disappear. (2) The initial mobile water saturation significantly reduces the width of the two-phase flow region once it is larger than the frontal water saturation of this region. (3) High water saturation leads to a long breakthrough time in a hot driving region and low oil recovery. (4) Large connate water saturation and porosity and small heated oil viscosity benefit the oil recovery before the breakthrough of the steam chamber.

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 score0.271
Threshold uncertainty score0.985

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 head0.034
GPT teacher head0.236
Teacher spread0.201 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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