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Record W2054480272 · doi:10.2523/iptc-16793-ms

Solvent-aided Steam-flooding Strategy Optimization in Thin Heavy Oil Reservoirs

2013· article· en· W2054480272 on OpenAlex
David W. Zhao, Jacky Wang, Ian D. Gates

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Petroleum Technology Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersPetroleum Technology Research CentreUniversity of Calgary
KeywordsPetroleum engineeringSteam injectionSolventSteam-assisted gravity drainageEnhanced oil recoveryBottom waterEnvironmental scienceMaterials scienceLight crude oilAsphaltOil sandsChemistryGeologyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Stochastic optimization based on a simulated annealing method were carried out to determine the optimum steam and steam-solvent flooding strategies in thin (4 m) heavy oil reservoir both in the absence and presence of a bottom water zone. The steam injection pressure optimization case determined a technically feasible operating strategy. However, the cumulative energy to produced oil ratio (cEOR) realized from the optimized process is high. In comparison, the solvent-aided steam optimization case achieved an operating stratety that obtains a much lower cEOR and cumulative water-to-oil ratio (cWOR) than those in the optimized injection pressure-only strategy. We observed that a solvent channel forms at the top of the reservoir after breakthrough of solvent to the production well. The formation of the solvent channel led to oil-solvent mixing at the periphery of the channel as well as heat transfer to oil beyond the channel, which leads to better recovery performance. In the the presence of a bottom water zone, the optimized steam injection pressure optimization strategy was found to perform poorly. However, the optimized solvent-aided strategy achieved superior economics. With solvent injection, the presence of the bottom water zone enhanced mixing of solvent and oil yielding better oil recovery performance. Background In Western Canada, about 80% of heavy oil resources are found in reservoirs less than 5 m thick (Adams 1982). Although currently commercial thermal-based techniques such as Steam-Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) are highly successful for recovering bitumen and heavy oil from thick pay zone (> 15 m), their application in thin heavy oil (<6 m) reservoirs are generally not thought to be economically viable. This is due to the high steam-to-oil ratio (SOR) which caused by significant heat losses to the overburden relative the amount of heat delivered to the oil which renders the processes uneconomic. Cold production (CP) employs small energy input. However, the average recovery factor is typically low, usually, between 3 to 8% of the Original Oil In Place (OOIP) (Adams 1982). By employing so called Cold Heavy Oil Production with Sand (CHOPS) technique, the recovery factor can reach as high as 15% (Pan et al. 2010). However, the formation of wormholes during CHOPS operation creates new challenges for applying follow-up processes to recover additional oil beyond CHOPS.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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