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Record W2143777384 · doi:10.2118/84119-ms

Field Application of Unconventional Foam Technology: Extension of Liquid CO2 Technology

2003· article· en· W2143777384 on OpenAlexaboutno aff
D. V. Gupta

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2003
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsViscosityFracturing fluidVolume (thermodynamics)Petroleum engineeringLiquid phaseLiquid waterMaterials scienceChemical engineeringChemistryThermodynamicsGeologyComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Liquid CO2-based systems have been pumped as fracturing fluids in North America since the early 1980s and liquid CO2/N2 system from 1994. The fluids have been used in more than 1000 wells in a variety of formations having permeability values from 0.1mD to 10 Darcies, depths in excess of 3000 m and bottom-hole temperataure (BHT) from 10 to 110 °C. The chemistry and physics of these fluids are intriguing and have been described before. Several attempts to increase the viscosity of liquid CO2 have been tried with little success. Here, a novel fluid is described that retains all the nondamaging aspects of liquid CO2 but with increased viscosity. This fluid is a foam of nitrogen in liquid CO2. It uses a CO2-soluble foamer that does not damage the formation and can be released into atmosphere without damaging the environment. The fluid does not include any other liquids, such as water, alcohol or hydrocarbons. The foam thus formed follows conventional foam physics (with respect to viscosity increase as a function of internal phase volume, etc.). Due to the limited amount of liquid CO2 used (around 20-25 volume% for a foam of internal quality of 75-80%), multiple jobs can be conducted on a single day making the system more cost-effective than the typical liquid CO2 fracturing system. A summary of the field use for shallow gas applications in Canada is described.

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 score0.203
Threshold uncertainty score0.398

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 head0.008
GPT teacher head0.240
Teacher spread0.231 · 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

Citations34
Published2003
Admission routes1
Has abstractyes

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