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Record W2973561721 · doi:10.2118/196049-ms

Evaluation of Air Injection into Mature SAGD Chambers as a Follow Up/Wind-Down Strategy, Using a Novel Large Scale 3-D Physical Model

2019· article· en· W2973561721 on OpenAlexaffabout
Ehsan Aminfar, Belenitza Sequera-Dalton, Sudarshan Raj Mehta, Gordon Moore, Matthew Ursenbach

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSteam injectionPetroleum engineeringOil sandsSteam drumSecondary air injectionSteam-assisted gravity drainageSaturation (graph theory)Boiler (water heating)Environmental scienceSurface condenserSuperheated steamWaste managementEngineeringMaterials scienceAsphalt

Abstract

fetched live from OpenAlex

Abstract The injection of air into mature steam chambers is a promising technology to reduce the steam-to-oil-ratios (SOR) in late stages of the Steam-Assisted-Gravity-Drainage (SAGD) recovery process in Athabasca oil sand reservoirs in Alberta, Canada. Air injection allows sustaining steam chamber pressures with reduced steam injection rates. The steam capacity that becomes available due to the replacement of steam with air in mature well-pairs or pads could serve new pads optimizing steam utilization and decreasing the overall environmental footprint of the project. A novel large scale three-dimensional (3-D) physical model was designed to evaluate the prospect of the "hybrid" air and steam injection technology in a SAGD configuration utilizing up to three well-pairs. This paper discusses the 3-D model design, commissioning, experimental procedure and main results of the first tests. For each test, the 3-D model was packed with a low oil saturation core or lean zone, representing the reservoir portion swept by steam, and a high oil saturation core or rich zone representing the un-drained zone between two coalesced steam chambers. These zones were made with preserved native "lean" and "rich" cores from Athabasca reservoirs. Once the model was packed, it was placed inside a pressure jacket where it was pressurized to reservoir pressure. Steam was injected into the model to develop a representative steam chamber in the lean zone. Once steam conditions were attained in the lean zone, steam injection was switched to air injection. Temperatures distributed in the 3-D model as well as injection and production pressures and produced gas compositions were monitored constantly and recorded during the test. Produced liquid samples were regularly captured and stored for subsequent analysis. Post-processing analyses of produced fluids and residual extracted core material allowed for determination of clean-burned zones, material balance, upgrading of the produced bitumen samples and efficiency of the process. High peak temperatures, gas compositions, clean-burned sand in post-test cores and significant oil production indicate the development of a high temperature combustion front in the 3-D experiments. The test results confirm the injection of air into mature SAGD chambers is a very promising method not only to reduce the cumulative steam-to-oil-ratios (CSOR) and to sustain the steam chamber pressures but also to increase oil production in SAGD late life.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.294
Teacher spread0.270 · 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 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

Citations7
Published2019
Admission routes2
Has abstractyes

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