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Record W1994062084 · doi:10.2118/08-11-63

Reducing Formation Damage with Microbubble-Based Drilling Fluid: Understanding the Blocking Ability

2008· article· en· W1994062084 on OpenAlexafffund
E. Kuru, N. Bjorndalen, E. Jossy, José M. Alvarez

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 CanadaUniversity of Texas at Austin
KeywordsMicrobubblesDrilling fluidPorous mediumMaterials scienceBlocking (statistics)ViscosityRheologyBlocking effectPorosityPetroleum engineeringVolumetric flow rateChemical engineeringDrillingGeologyComposite materialMechanics

Abstract

fetched live from OpenAlex

Abstract Commonly, bridging materials used to reduce formation damage and mud losses in the near wellbore region consist of solid particles. These particles need to be removed after drilling through processes such as acidizing. Microbubble-based drilling fluids utilize gas bubbles to bridge the pores instead of solid particles. These microbubbles can be removed during the initial stages of production, thereby, reducing the costs associated with stimulation processes. Although there has been some work done on the flow of microbubbles through porous media, little is known regarding what conditions (e.g. viscosity, fluid composition, pressure) determine whether the microbubbles will or will not block the pores. Both the microbubble diameter and the rock pore size distribution play roles in determining sealing of the pores. In order to gain an appreciation of the pore blocking mechanism, experiments were conducted using micro-model cells to visually understand the blocking mechanism. The composition of the fluid is varied, as well as the flow rate at which the fluid is injected. Through this, the pressure at which the microbubbles invade the medium for various compositions of the fluid for pore blocking is determined. The average microbubble size at the invasion pressure is compared to the average pore size of the porous medium. By understanding the extent of the microbubble invasion under varying conditions, a greater comprehension of the pore blocking mechanism can be established. As well, the success rate of applying the microbubble system to various types of reservoirs can be evaluated. Introduction Common blocking agents used in the oil and gas industry to reduce formation damage and mud losses while drilling and to block highly permeable streaks in the reservoir during production can be composed of gels, solid particles, emulsions and foams(1). The ability of these fluids to block specific areas of the reservoir is determined through the reduction of permeability in high permeable zones and the ability to place the fluid in the correct area within the reservoir. Specifically, many studies have been conducted on the blocking ability of foams in porous media(2–7). When discussing blocking ability, most of these studies are concerned with blocking the flow of gas(3). Albrecht and Marsden(3) found that, with an increase in surfactant concentration, the blocking effect was greater. The efficiency of foams to block gas propagation in porous media with crude oil was studied by Hanssen and Dalland(4). They tested many different surfactants and found that there was no correlation between gas blocking and interfacial tension and that only a few foams blocked the samples tested. They stated that the foam blocking is a complex phenomenon. Aarra and Skauge(5) and Aarra et al.(8) tested foams for gas mobility control in the North Sea with successful results. Nimir and Seright(6) compared the placement efficiency of foams to gels as blocking agents in various cores ranging in permeability from 7.5 to 900 mD. They used a C14–16α-olefin sulfonate and found that the foam was a better blocking agent when the low permeable zone is less than 7.5 mD and the high permeable zone is more than 80 mD.

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 score0.259
Threshold uncertainty score0.544

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 integrity0.0000.001
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 head0.014
GPT teacher head0.170
Teacher spread0.156 · 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

Citations13
Published2008
Admission routes2
Has abstractyes

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