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Record W2574726699 · doi:10.1016/j.petrol.2017.01.029

Characterization of gas-oil flow in Cyclic Solvent Injection (CSI) for heavy oil recovery

2017· article· en· W2574726699 on OpenAlexafffund
Sam Yeol Hong, Fanhua Zeng, Zhongwei Du

Bibliographic record

VenueJournal of Petroleum Science and Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
FundersPetroleum Technology Research Centre
KeywordsGas oil ratioSolventPetroleum engineeringChemistryWet gasFossil fuelLight crude oilFlow (mathematics)GeologyOrganic chemistryMechanics

Abstract

fetched live from OpenAlex

Cyclic Solvent Injection (CSI) has emerged as an effective follow-up process to the primary cold production, namely, Cold Heavy Oil Production with Sand (CHOPS). In this recovery process, the solvent is designed to maintain a strong nature of gas at in-situ conditions. As a result, the porous medium is spatially divided into two zones with differing fluid properties, which are gas zone, also called solvent chamber, and heavy oil zone. The CSI process is governed by the gas-oil flow as the solvent chamber is predominated by free gas-oil flow and the heavy oil zone by dispersed gas-oil flow (i.e. foamy oil flow). The gas-oil flow in CSI considerably differs from that in heavy oil solution gas drive, and therefore, needs to be investigated separately. The differences mainly arise from the origin of free gas. In CSI, the free gas originates at the solvent chamber, whereas in heavy oil solution gas drive, it evolves from solution gas. The free gas, in accordance with where it originates, yields occurrence time and quantity that have different dependency on the pseudobubblepoint pressure of oil. Consequently, the gas-oil flow in CSI results in the characteristics far more susceptible to the quantity of free gas and the nonequilibrium nature of foamy oil than heavy oil solution gas drive. This study is aimed at characterizing the gas-oil flow in CSI under the effects of pressure depletion rate as well as the solvent chamber. To fulfill this objective, the gas-liquid relative permeability curves were inferred with the use of numerical simulations and modified fractional flow models. The numerical simulations were carried out to history-match seven lab-scale CSI tests performed at different pressure depletion rates. The modified fractional flow models were applied to describe the foamy oil flow. The distinct characteristics of the gas-oil flow were examined based on sensitivity analysis and comparison to the previous findings on heavy oil solution gas drive. The results suggest that, at low pressure depletion rates, the gas-oil flow in CSI yield the characteristics that have also been observed in heavy oil solution gas drive. At sufficiently high pressure depletion rates, however, the free gas that exists even when the dispersed gas bubbles are immobile results in the different behavior of critical gas saturation and gas phase mobility. The solvent chamber misleads the gas-liquid relative permeability curves if the critical gas saturation is too high to properly describe the simultaneous flow of free gas and foamy oil. The solvent injectivity is also affected by the pressure depletion rate due to the foamy oil that has remained as unproduced in the solvent chamber during a previous production period.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.226
Teacher spread0.218 · 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".

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Citations24
Published2017
Admission routes2
Has abstractno

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