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Record W2076957565 · doi:10.2118/06-05-02

Application of CO-Foam as a Means of Reducing Carbon Dioxide Mobility

2006· article· en· W2076957565 on OpenAlexafffund
Faisal Khalil, K. Asghari

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Regina
FundersPetroleum Technology Research Centre
KeywordsBrinePulmonary surfactantCarbon dioxidePorous mediumCarbonateEnhanced oil recoveryChemical engineeringMaterials sciencePorosityChemistryComposite materialMetallurgyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Reducing the mobility of carbon dioxide through co-injection of CO2 and a suitable surfactant solution to form a CO2-foam system is a promising method for improving the oil recovery in carbon dioxide flooding projects. This paper presents the results of a set of experiments on screening and selecting a suitable surfactant for CO2 -foam purposes in a carbonate porous medium, as well as the effect of various parameters on the mobility of the CO2-foam system. Four surfactants were examined and the one that performed best throughout the screening experiments was used in the subsequent flow experiments. The surfactants tested were Surfonic N- 95, Surfonic L24–9, Bio-Terge AS-40, and Chaser CD-1045. The screening criterion selected was the fall in foam height with time at 60 ° C for 0.1 wt% solution of the above mentioned surfactants. Chaser CD-1045 performed best in all screening tests and was used during the flow experiments. Flow experiments were conducted through a porous medium made of crushed carbonate at pressures of 8,270 kPa and 10,336 kPa, and temperatures of 22 ° C and 50 ° C. Mobility of CO2 -brine (simulating the WAG process) and CO2-surfactant systems were compared through a series of experiments. The effect of operating pressure and temperature, brine concentration, and the ratio of the amount of CO2 to total foam (i.e., foam quality) on the mobility of a CO2-foam system were investigated and results are presented. The results indicate that additional oil is recoverable for CO2-foam vs. the co-injection of CO2 and brine simulating the WAG process. Introduction From the pore-scale point of view, dense carbon dioxide is an ideal displacement fluid for many crude oils because it can achieve miscibility with oil through a multi-contact miscibility process under the pressure and temperature conditions of a wide range of reservoirs. However, even when pressure conditions for miscibility are met, this high microscopic sweep efficiency is not often approached in reservoir operations due to the non-uniformity of the flow patterns. Large-scale reservoir heterogeneities, such as fractures or high-permeability streaks, cause early breakthrough of injected carbon dioxide, which will reduce oil recovery efficiency. One effective way of increasing the ultimate oil recovery under CO2 flooding conditions is by reducing the mobility of the injected carbon dioxide. The most common method for achieving this goal is through the injection of slugs of CO2 and water alternatively (i.e., the WAG process). During the WAG process, water reduces the mobility of carbon dioxide; but it also traps oil, increases water flow, and decreases extraction of hydrocarbons from oil by carbon dioxide(1). Another method for reducing the mobility of carbon dioxide is the CO2-foam technique. In this method, a surfactant solution is injected along with carbon dioxide into the reservoir. This combination forms foam in the reservoir, and the presence of foam reduces the mobility of carbon dioxide considerably. However, for any CO2 -foam project, there are challenges that must be met.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.003
GPT teacher head0.204
Teacher spread0.201 · 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

Citations53
Published2006
Admission routes2
Has abstractyes

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