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Record W3198475471 · doi:10.2118/205158-pa

An Experimental Study of Steam-Solvent Coinjection for Bitumen Recovery Using a Large-Scale Physical Model

2021· article· en· W3198475471 on OpenAlexaff
Kai Sheng, Ryosuke Okuno, Abdullah Al-Gawfi, Petro Nakutnyy, Muhammad Imran, Kazunori Nakagawa

Bibliographic record

VenueSPE Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsSteam-assisted gravity drainageAsphaltOil sandsSolventSteam injectionPetroleum engineeringAsphaltenePorositySoil vapor extractionPermeability (electromagnetism)Materials scienceChromatographyChemistryMineralogyGeologyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Summary In this paper, we present a solvent-assisted steam-assisted gravity drainage (SA-SAGD) experiment with multicomponent solvent (i.e., condensate) using a large physical model. The sandpack for the experiment had a porosity of 0.33 and a permeability of 5.6 darcies in the cylindrical pressure vessel that was 1.22 m in length and 0.425 m in internal diameter. The sandpack was initially saturated with 93% Athabasca bitumen and 7% deionized water. The main objective of this research was to study the in-situ thermal/compositional flow and produced bitumen properties in SA-SAGD with condensate. After the preheating of the sandpack for 24 hours, SA-SAGD with 2.8-mol% condensate was performed at 50 cm3/min (cold-water equivalent) at 3500 kPa for 3 days. The experimental data of production, injection, and temperature distribution were recorded. Also, 10 samples of produced oil were taken and analyzed for density and asphaltene content. The sandpack was excavated after the experiment to analyze the asphaltene content in the remaining oil at different locations. A numerical simulation model was calibrated based on the data of material balance and temperature distribution, and it was validated with properties of the produced and excavated samples. The simulation model used fluid models based on experimental data of viscosities, densities, and bubblepoints for four condensate/bitumen mixtures. Results showed that SA-SAGD was efficient in bitumen recovery with a cumulative steam-to-oil ratio (SOR) that was two to three times smaller than that in SAGD using the same physical model. Detailed analysis of the calibrated simulation model indicated that SA-SAGD enabled the steam chamber to expand more efficiently with a smaller amount of water throughput than SAGD. Volatile solvent components tended to remain in the chamber, and the condensed solvent components acted as a miscible carrier for bitumen components. The analysis further showed that the more efficient oil recovery in SA-SAGD occurred with predominantly cocurrent flow of oil and water near the chamber edge. SA-SAGD recovered a larger amount of asphaltene components (i.e., less in-situ upgrading) than SAGD likely because of its lower chamber temperature, shorter production period, and enhanced local displacement efficiency.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.522

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 head0.022
GPT teacher head0.310
Teacher spread0.288 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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