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Record W2027136622 · doi:10.2118/2005-073

Numerical Simulation of Foamy Oil Depletion Tests

2005· article· en· W2027136622 on OpenAlexaffabout
Zhangxin Chen, Brij Maini

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer sciencePetroleum engineeringGeology

Abstract

fetched live from OpenAlex

Abstract Foamy oil flow, a phenomenon associated with primary production of heavy oil under solution gas drive, is a widely accepted explanation for the high recovery factors observed in some heavy oil reservoirs. A number of theoretical, experimental and field studies related to the subject have been conducted to develop an understanding of the mechanisms involved. However, our understanding of the phenomenon is still incomplete. One specific area that remains uncertain is the best methodology for reservoir simulation for predicting the future performance. The objectives of this study were to simulate several depletion experiments carried out in a 2 m long sand-pack and compare the results obtained with a Black Oil Model (IMEX) and a reaction rate based Foamy Oil Model that is available in CMG's Advanced Thermal Model (STARS). The results show that the foamy oil model provides more accurate matching of the experimental results compared to the black oil model. Sensitivity studies done with both models show that:relative permeabilities to gas and oil have very significant effects on oil production,reservoir fluids properties like mole fraction or ratio of original dissolved gas to dead oil, viscosity of the dead oil and the liquid-phase viscosity of gas have meaningful effects on oil production, andthe distributions of oil, dissolved gas, free gas and pressure along the sand-pack changes significantly with depletion rates. ased on the history matching and sensitivity analysis, the influences of certain reservoir parameters, such as oil viscosity, pressure depletion rates, critical gas saturation, initial pressure and solution gas to oil ratio (GOR) on the performance of foamy solution gas drive were examined. Introduction There is considerable world-wide interest in economically producing heavy oil and oil sands. There are vast heavy oil and oil sands resources in the world, which are estimated between 6.04×1011 m3 and 9.86×101011 m3 in total. Of these Canada holds about 4.69×101011 m3 or 42% of the worldwide total1 and Venezuela holds about 27% of the worldwide total. According to EUB 2004 statistics reports2, the initial volumes of in-place heavy oil and bitumen in Alberta were estimated to be 2.052×10109 m3 and 258.9×10109 m3 respectively. Currently, most of the heavy oil resource in Canada is exploited by using primary production scheme with expected recovery factor of about 5%. However, several heavy oil reservoirs in the west anada and Venezuela and China under solution gas drive have shown anomalous behavior: high oil production rates, low GOR and high recovery. Smith3 reported that the primary recovery of heavy oil under solution gas drive in several Canadian reservoirs was much higher than what would be predicted using the conventional method. Since then the primary recovery of heavy oil under solution gas drive has received considerable attention in the literature. To explain this unusual recovery behavior, three fundamental mechanisms have been postulated in recent publications. Geomechanical effects, fluid flow effects, and the combination of geomechanical and fluid flow effects.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations31
Published2005
Admission routes2
Has abstractyes

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