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Record W2003350189 · doi:10.2118/2005-220

CO Energized Hydrocarbon Fracturing Fluid: History and Field Application in Tight Wells in the Rock Creek Oil, Dunvegan, Glauconite and Belly River Gas Formations

2005· article· en· W2003350189 on OpenAlexaboutno aff
Neha Gupta, T.T. Leshchyshyn

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCitationGeologyGlauconitePetroleum industryPetroleumPetroleum engineeringArchaeologyGeochemistryMining engineeringLibrary scienceGeographyPaleontologyComputer science

Abstract

fetched live from OpenAlex

Abstract Gelled hydrocarbon has been used as a fracturing fluid from the early days of fracturing. Its use internationally has been prevalent; and 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 lowpressure tight gas wells, where the use of CO2 energized gelled hydrocarbon fluids have been very successful. The chemistry of these fluids and their application is discussed. The energized fluids have been used varied formations with permeabilities from 0.1 mD to 10 D, depths in excess of 3000 m and BHT from 10 to 110 °C. Initial and longer-term production data from wells treated with the energized fluid will be compared to wells treated with conventional gelled hydrocarbon fluids to show the effectiveness of the system in tight gas applications. The case study will include wells treated and producing from the Rock Creek oil, Dunvegan, Glauconite and Belly River gas formations in the Western Canadian Sedimentary Basin (WCSB). Observations on proppant selection and in-situ closure stress are made in addition to the fracturing fluid selection. Introduction Natural gas currently accounts for 22% of world wide energy consumption. As worldwide use of natural gas is increases to meet energy demand, interest in unconventional gas increases. This includes coal-bed methane, tight-sand gas, shale gas and gas hydrate wells. Commercial production of unconventional gas is still in its infancy with development of tight gas, coal-bed methane and shale gas performed mainly in North America. Resource assessment data of unconventional gas varies widely; however, it is universally accepted that the unconventional gas resource base, excluding gas hydrates, is more than twice that of conventional gas. Gas hydrates are excluded due to limited technology and success at developing the resource. In North America, gas resource from tight sands is estimated to be around 1371 Tcf (1). Unconventional gas production was only 1 Tcf / year in the 1970s, but has increased to around 4 Tcf / year in 1997 which accounted for 20% of the national gas consumption (2). Almost 70% of unconventional gas production is from tight sands. The definition of tight gas is somewhat arbitrary, but is generally accepted to mean from formations with an average air absolute permeability less than 20 mD. In-situ permeabilities in these types of reservoirs are generally less than 1 mD and can range down into the micro-Darcy range (10–6 D) in many cases. Using appropriate drilling, completion and in some cases large-scale fracturing techniques, operators have succeeded in obtaining economic production rates from formations exhibiting in-situ matrix permeabilities as low as 10–6 D. In many cases, gas may exist in such low permeability formations, but its production is challenged due to adverse capillary forces, high in-situ saturations of trapped water, and in some cases, the presence of liquid hydrocarbons. If these saturations are too high, economic production from the zone is difficult without appropriate fracturing techniques. Tight gas reservoirs are susceptible to formation damage during drilling and completion operations. Low permeability formations tolerate only minimal damage.

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 score0.312
Threshold uncertainty score0.930

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 head0.192
Teacher spread0.187 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

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