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Record W2177477571 · doi:10.2118/00-11-05

Effect of Solution Gas in Oil on CO2 Minimum Miscibility Pressure

2000· article· en· W2177477571 on OpenAlexaffabout
Mingzhe Dong, R.K. Srivastava

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsPetroleum engineeringMiscibilityEnhanced oil recoveryGas oil ratioCarbon dioxideOil fieldLight crude oilBubble pointPetroleum reservoirEnvironmental scienceBubbleChemistryMaterials scienceGeologyPolymerMechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this study, a rising bubble apparatus (RBA) was used to determine the CO2 MMP for various oils. RBA tests permit direct observation of changes in bubble behaviour and thus offered insight into the phase behaviour for the CO2-reservoir fluid system. The CO2 MMPs were estimated for two Steelman reservoir fluids with a high gas-oil ratio, the partially flashed reservoir fluids, and the dead oils. The composition of solution gas of each partially flashed reservoir fluid was determined and the effects of different gas components were analysed. The MMP was also determined and discussed for Weyburn reservoir fluids which had a low gas-oil ratio with pure and impure CO2. The results of this study demonstrated that the effect of solution gas in oil on CO2 MP could be significant. Furthermore, achieving a miscible CO2 flood (in a reservoir with a Steelman-like reservoir fluid) could be possible at a lower operating pressure than the measured CO2 MMP, by partially depleting the reservoir. This may be the only option for some reservoirs which cannot sustain the relatively high pressure required for achieving miscibility. Introduction Carbon dioxide flooding is a proven oil recovery process(1). Over the last decade, carbon dioxide injection has become the leading enhanced oil recovery (EOR) process for light oils(2). CO2 injection can prolong, by 15 to 20 years, the production life of light oil fields nearing depletion under waterflood, and may recover 15 to 25% of the original oil in place. More than 20 years of field experience of CO2 injection has advanced the CO2 technology. CO2 injection can be introduced gradually and use some of the same equipment currently in place for waterflooding. Saskatchewan's light and medium oil resource, representing nearly 45% of proven reserves, has been on waterflood for over 30 years and is fast approaching its economic limit of production(3). For the light and medium oil reservoirs in Saskatchewan, carbon dioxide or hydrocarbon injection is considered to be the most effective EOR process(4–6). These gases can be injected into the reservoir to develop miscible or immiscible conditions with the oil depending upon the operating pressure. Carbon dioxide is preferred over hydrocarbon gases (e.g., ethane, propane) because it is cheaper, has higher density, and offers environmental benefits by providing storage for CO2 in the reservoir. The Saskatchewan Research Council (SRC) is conducting a comprehensive research program to assess the suitability of miscible CO2 displacement for reservoirs in southeast Saskatchewan and to optimize the field operating procedures. The first step in determining if a field is a viable CO2 flood candidate is to conduct a screening study to provide a reasonable estimate of CO2 injection performance. The minimum miscibility pressure (MMP) is a key parameter used in the assessment. It has been recognized that the CO2 MMP for a reservoir oil depends on the reservoir temperature, oil composition, and the purity of injected CO2. The minimum miscibility pressure increases with increasing reservoir temperature.

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)0.001
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 score0.848
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.203
Teacher spread0.200 · 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

Citations64
Published2000
Admission routes2
Has abstractyes

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