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Record W1996243055 · doi:10.2118/01-02-04

Micellar Flooding and ASP-Chemical Methods for Enhanced Oil Recovery

2001· article· en· W1996243055 on OpenAlexaff
Sara Thomas, S.M. Farouq Ali

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsPeraso Technologies (Canada)
Fundersnot available
KeywordsResidual oilPetroleum engineeringFlooding (psychology)Pulmonary surfactantEnhanced oil recoveryPorous mediumEnvironmental scienceOil in placeOil fieldChemistryGeologyChemical engineeringPorosityPetroleumGeotechnical engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Chemical flooding methods hold particular attraction for recovering the "residual oil" left in the reservoir after waterflooding. This paper describes and compares the results for two promising methods, viz. micellar flooding and alkaline-surfactant-polymer (ASP) flooding processes. Both of these methods have been tested successfully in the field, notably micellar flooding. Laboratory results are described for micellar floods in consolidated sandstone cores as well as in unconsolidated sand packs, including a three-dimensional model, equipped with horizontal or vertical wells. Floods were also carried out in unconsolidated cores using combinations of an alkali, surfactant and a polymer. Individual slugs were injected sequentially in some of the experiments, while the three components were mixed and injected as a single slug in other experiments. Oil recoveries in the two cases were similar. Results for the two processes are compared and contrasted, showing that, on the basis of oil volume recovered per unit mass of the chemical used, the two processes are similar, with micellar flooding having an edge. However, on the basis of total oil recovery, micellar flooding is the superior process, with oil recoveries ranging from 50 to 80﹪ of the oil left in the porous medium after a waterflood. Practical implications of the results are discussed. Introduction Among chemical flooding methods, micellar flooding and alkaline- surfactant-polymer (ASP) flooding processes are particularly effective for recovering a large fraction of the conventional oil (25 °CDATA[API, or higher) left in the reservoir after a waterflood, which could be as much as 60﹪ of the original oil in place. Many field tests of the micellar flooding process and several of ASP have established the effectiveness of these methods for mobilizing waterflood residual oil. The present laboratory study compares and contrasts the two processes, based on tertiary floods in sand packs and Berea sandstone cores. A number of investigators have noted the use of an alkali for reducing the divalent ion content and increasing the negative charge of the rock with a view to reducing chemical loss(1,2). Surkalo(3) reported the alkaline-surfactant-polymer (ASP) process as an alternative to micellar flooding. Several field tests have also been reported. Another function of alkali, if the Acid No. of the crude oil is large enough (> 0.5 mg KOH/g crude oil) is that the alkali may react with the acid components to form a surfactant in situ. Other factors, such as gravity segregation of alkali solutions, rate of diffusional and mechanical mixing, and subsequent mixing of surfactant formed would limit the effectiveness of this mechanism. The Process Chemical flooding methods are based on improving the mobility ratio, i.e., making the mobility of the displacing flood less than or equal to the mobility of the displaced fluid, and increasing the capillary number, mainly by making the interfacial tension (IFT) between the displacing and the displaced phases small, usually by about 1,000 fold. Other effects are also present, such as formation of macro- and microemulsions, formation of precipitates, wettability changes, relative permeability shifts, etc. Macroemulsions may improve the mobility ratio through drop entrainment and entrapment. At the same time, surfactant adsorption occurs on the rock surface.

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 score0.374
Threshold uncertainty score0.837

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 integrity0.0000.001
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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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

Citations37
Published2001
Admission routes1
Has abstractyes

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