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Record W4242007444 · doi:10.2174/2211334710902010001

Insight into the Chemistry of Surfactant-Based Enhanced Oil Recovery Processes

2009· article· en· W4242007444 on OpenAlexaff
Benyamin Yadali Jamaloei

Bibliographic record

VenueRecent Patents on Chemical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPulmonary surfactantResidual oilEnhanced oil recoveryOil in placeChemistryPetroleum engineeringPetroleumChemical engineeringOrganic chemistryGeologyEngineering

Abstract

fetched live from OpenAlex

During the past several decades, significant and considerable research has been carried out on secondary and tertiary recovery of trapped residual oil remaining within the producing formations underground despite the efficient, current primary production strategies and methods. Methods have been sought of increasing oil recovery, while revamping and improving the economic viability and efficiency of operations. One method that has received much attention and intensive study over these past decades is the use of surfactant-based chemical flooding. Initial patents and laboratory tests have shown conclusively that chemical solutions and slugs including surfactants, remove considerable oil from the porous medium normally trapped after initial waterflooding. Oil recovery processes by means of surfactant micellar solutions or microemulsions have included the injection of slugs of varied compositions. Patents on surfactant-based enhanced oil recovery processes (such as dilute surfactant flooding, surfactant/polymer flooding, and alkaline/surfactant/polymer flooding) have been issued, starting from the 1920s and particularly after the 1960s, when the technology was put on a scientific basis. These patents were generally concerned with the chemistry of surfactant-based enhanced oil recovery processes including envisioning the use of chemical solutions (slugs) to decrease the surface tension between oil and the flooding medium, screening of surfactants for oil recovery efficiency, chemical (surfactant) slug designs and formulation to mobilize residual oil, documenting petroleum sulfonates useful in surfactant flooding, and other important factors in the chemistry of surfactant-based chemical flooding processes. Moreover, evaluation and determination of optimum chemical systems (slugs) for the best economics for a specific field application have been disclosed and documented in several large oil companies patents in the surfactant-polymer area. In this article, a full-fledged review of the patents on different aspects of the chemistry of surfactant-based enhanced oil recovery processes is presented. Keywords: Surfactant, enhanced oil recovery, chemistry, chemical slug, patent

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.204
Teacher spread0.196 · 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 designObservational
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

Citations22
Published2009
Admission routes1
Has abstractyes

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