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Record W4247349603 · doi:10.2118/2004-077

Evaluation of Oil Recovery Performances of Surfactants Using Organic Conception Diagrams

2004· article· en· W4247349603 on OpenAlexafffund
T. Babadagli, Y. Boluk

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPetroleum engineeringComputer scienceProcess engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract Capillary (spontaneous) imbibition recovery in naturally fractured reservoirs can be enhanced by adding surfactant into injected water. Selection of proper surfactant type and concentration is essential in this process as the reduction in IFT by addition of surfactant does not always yield an incremental recovery over waterflooding. Capillary imbibition experiments conducted on sandstone, chalk, and limestone samples using different oils (crude and processed with different viscosities) and different surfactant solutions were evaluated for surfactant screening. In this evaluation IFT, surfactant type and concentration were considered. In addition to these, a new technique has been adopted to facilitate the surfactant screening process. This technique is based on assigning inorganic and organic property values and plotting Organic Conception Diagrams (OCD) for surfactants. OCD defines the property of a compound in terms of physical chemistry in such a way that the property that depends much on Van der Waals force is called "organic" and the one that depends much on electric affinity is called "inorganic". The OCD has been widely applied especially in the field of environmental and pharmaceutical chemistry as means that express the property of organic materials that feature relatively complex interactions. By using the OCD, hydrophilic and lipophilic characters of surfactants have been established to evaluate their potential to minimize oil/water interfacial tension. Correlations between the capillary imbibition recovery performance and the properties of surfactant and oil (organic value (OV), inorganic value (IV), and IFT of surfactant solutions, oil viscosity, and surfactant type) were sought. These correlations are expected to be useful in selecting the proper surfactant for improved oil recovery as well as identifying the effects of surfactant properties on the capillary imbibition performance. Introduction Using surfactant solutions as a displacement fluid to recover matrix oil in naturally fractured reservoirs (NFR) has gained a great deal of attention in recent years. Capillary imbibition recovery can be enhanced by the addition of surfactant into water to reduce the IFT.1 Depending on the size and wettability characteristics of the reservoir matrix, the gravitational forces might play a role in the matrix recovery when the IFT is lowered using surfactant solutions as an aqueous phase.2–5 A group of studies tested the capillary imbibition recovery performance of sandstone matrix when surfactant solutions are used1–10. Typically an increase in the ultimate recovery with lowered IFT was observed. The imbibition rate also changes with reduced IFT. Another group of study used carbonate rocks.1,8,11,12 Due to less water-wet character of this type of rocks, in many cases, very low imbibition recovery was obtained with brine. Reduction of IFT by addition of surfactant yielded significant increase in the recovery due to enhanced capillary imbibition and additional contribution due to gravity effect.1,7,8 More attention was devoted to chalky carbonates as they are found more responsive to the imbibition recovery compared to the dolomitic carbonates or strongly water-wet sandstones.13–21 It was observed that the response to the surfactant capillary imbibition recovery could be very different depending on the rock type and fluid properties even if the same surfactant type and concentration are used.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.259
Teacher spread0.228 · 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 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

Citations0
Published2004
Admission routes2
Has abstractyes

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