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Record W2095269566 · doi:10.2118/95061-ms

CO2 Energized Hydrocarbon Fracturing Fluid: History & Field Application in Tight Gas Wells in the Rock Creek Gas Formation

2005· article· en· W2095269566 on OpenAlexaboutno aff
D. V. S. Gupta, T. T. Leshchyshyn

Bibliographic record

VenueSPE Latin American and Caribbean Petroleum Engineering Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyPetroleum engineeringHydrocarbonFracturing fluidTight gasNatural gas fieldNatural gasFossil fuelHydraulic fracturingGeochemistryChemistryWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract Gelled hydrocarbon has been used as a fracturing fluid from the early days of fracturing. Over the years, its use has diminished in the U.S. However, its use internationally continues to the present day in Canada, South America, Russia and East Asia. The paper will discuss the use of gelled oil fluids in Canada, particularly in low pressure tight gas wells, where the use of CO2-energized gelled hydrocarbon fluids has been very successful. The chemistry of these fluids and their application is discussed. The energized fluids have been used in a variety of formations and in wells of permeabilities from 0.1 mD to 10 Darcies, depths in excess of 3000 m and BHT from 10 to 110°C. Initial and longer-term production data from wells fractured with the energized fluid will be compared to wells fractured with conventional gelled hydrocarbon fluids to show the effectiveness of the system in tight gas applications. The case study will include gas wells fractured and producing from the Rock Creek formation east of Edson, Alberta, Canada from 47-10W5 to 56-15W5 in the Western Canadian Sedimentary Basin (WCSB). Observations on proppant type and proppant amount are made in addition to comments on the fracturing fluid selection.

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 score0.101
Threshold uncertainty score0.985

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 head0.006
GPT teacher head0.190
Teacher spread0.183 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

Explore more

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