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Record W2016697443 · doi:10.2118/08-11-15-cs

Physico-Chemical Characterization of Aphron-Based Drilling Fluids

2008· article· en· W2016697443 on OpenAlexafffund
N. Bjorndalen, Ergün Kuru

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDrilling fluidUnderbalanced drillingPetroleum engineeringDrillingRheologyColloidBubbleFiltration (mathematics)Pulmonary surfactantPressure dropMaterials scienceViscosityWell drillingChemical engineeringGeologyComposite materialMechanicsEngineering

Abstract

fetched live from OpenAlex

Abstract Colloidal gas aphron-based drilling fluids are designed to minimize formation damage by blocking the pores of the rock with microbubbles, which can later be removed easily when the well is open for production. Sizing colloidal gas aphron (CGA) bubbles in accordance with the rock pore size distribution is essential for effective sealing of the pores during drilling. The physical properties (i.e. viscosity, density, fluid loss, etc.) of the CGA-based drilling fluids also need to be understood in order to use these fluids more effectively. In this study, the physical properties of colloidal gas aphron-based drilling fluids are investigated. The results of rheology, API filtration loss and density measurement tests using various CGA-based drilling fluid formulations are presented. The effects of polymer and surfactant concentration, surfactant type, shear rate, mixing time and water quality on the CGA bubble size have been studied. Results of CGA bubble size characterization experiments are also reported. Introduction Colloidal gas aphron-based drilling fluids have recently been used for drilling at-balance in an attempt to eliminate the problems associated with overbalanced and underbalanced drilling. In order to achieve an at-balance drilling situation, the fluid pressure must be maintained at a level greater than the formation pressure, but the difference should be kept at a minimum level to avoid invasion of the fluid into the formation(1). Colloidal gas aphron drilling fluid simulates such a situation by building a bridge in front of the pores of the rock. It is believed that this bridge stabilizes the rock while allowing minimal damage to the formation. This system has been successfully implemented in high-angle and horizontal well drilling in highly depleted reservoirs(2), as well as with vertical wells. Simply put, aphrons are bubbles, approximately 10 to 100 microns in diameter. The term colloidal gas aphrons was first used by Sebba(3). Like regular foams, aphrons are typically composed of a gaseous (colloidal gas aphrons) or liquid (polyaphron) core. Unlike foams, however, aphrons have a thin aqueous protective shell. Aphron stability is determined by the rate of mass transfer between the viscous water shell and the bulk phase. This transfer is known as the Marangoni effect(3–5). If the mass transfer rate is high, aphrons will be unstable. Therefore, the shell fluid is designed to have certain viscosity to minimize the Marangoni effect. The shell is composed of an inner layer and an outer layer. Figure 1 illustrates a typical aphron. The inner layer consists of surfactant molecules which supports and separates the air core from the viscous layer. The outer layer, which also supports the viscous layer, is hydrophobic outwards and hydrophilic inwards. Since this bubble is in contact with the bulk water, it is believed that there is another layer in which the surfactant molecules are hydrophobic inwards and hydrophilic outwards. This indicates that there is a region in between the aphron outer shell and the bulk phase layer where a hydrophobic globule will be comfortable and, therefore, oil can adhere to the gas aphron(3).

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 score0.161
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.005
GPT teacher head0.156
Teacher spread0.151 · 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

Citations56
Published2008
Admission routes2
Has abstractyes

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