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Record W2002357822 · doi:10.2118/97211-ms

Surfactant Gel Foam/Emulsion: History and Field Application in Western Canadian Sedimentary Basin

2005· article· en· W2002357822 on OpenAlexaboutno aff
D. V. S. Gupta, T. T. Leshchyshyn, Barry Hlidek

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2005
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPulmonary surfactantEmulsionPetroleum engineeringFracturing fluidSedimentary rockGeologyMethaneEnhanced oil recoveryWell stimulationPermeability (electromagnetism)HydrocarbonChemical engineeringMineralogyMaterials scienceChemistryPetroleumGeochemistryReservoir engineeringMembraneOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Surfactant gels were introduced into the Western Canadian Sedimentary Basin (WCSB) as fracturing fluids in 1998. These fluids proved to be successful for propped fracture stimulations in shallow gas wells. About the same time, it was found that surfactant gels could be foamed with nitrogen or emulsified with a high quality (volume fraction) of liquid CO2. Since their inception in the Canadian market, surfactant gel foams/emulsions have been used in coal-bed methane (CBM) and also in the stimulation of deeper, higher temperature applications. This paper will discuss the use of these fluids in the WCSB as well as the chemistry of the fluids, laboratory conductivity measurements which show proppant pack retained conductivities of greater than 96%, and their application in more than 3,100 oil and gas producing zones. The fluids have been used in a variety of formations and in wells with permeability's from 0.1 millidarcies to 10 Darcies, depths in excess of 3000 m (10000 feet) and bottom-hole temperatures up to 100 °C (212 °F). For water-sensitive formations, foamed surfactant gel fracturing costs are significantly less in comparison to hydrocarbon, CO2, or methanol-based fluids.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.971

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)0.0000.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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 designNot applicable
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

Citations52
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueSPE Annual Technical Conference and ExhibitionSame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207