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Record W2536699113 · doi:10.2118/1115-0078-jpt

Case Study Shows Benefits of Applying Hollow Glass Spheres to Drilling Fluids

2015· article· en· W2536699113 on OpenAlexaboutno aff
Adam Wilson

Bibliographic record

VenueJournal of Petroleum Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDrilling fluidDrillingPetroleum engineeringHydrostatic pressureEmulsionGeologyMechanical engineeringEngineeringMechanicsChemical engineeringPhysics

Abstract

fetched live from OpenAlex

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 174010, “Hollow-Glass-Sphere Application in Drilling Fluids: Case Study,” by Arminder Minhas, SPE, Halliburton; Brandon Friess, SPE, Farid Shirkavand, and Barry Hucik, Seven Generations Energy; Teresa Pena-Bastidas, 3M Canada; Bradley Ross, SPE, Halliburton; and Shawn Servinski and Frank Angyal, Seven Generations Energy, prepared for the 2015 SPE Western Regional Meeting, Garden Grove, California, USA, 27–30 April. The paper has not been peer reviewed. In an effort to optimize drilling operations and economics, an operator examined the effect of adding hollow glass spheres (HGSs) directly to the drilling fluid instead of performing underbalanced drilling. Both nitrogen and HGSs were believed to reduce hydrostatic pressure of the mud column in the hole, resulting in higher drilling rates of penetration (ROPs) and reduced mud losses to the wellbore. This paper provides information on HGSs as an economic alternative to nitrogen to help reduce the hydrostatic pressure of invert-emulsion drilling fluids. Introduction This case study focuses on the application of HGSs in two different sections of the drilling operation—a horizontal section and a vertical intermediate section. Data from a base-case horizontal section using an all-oil drilling fluid were compared with data of another well on the same drilling pad using the same fluid with the addition of HGSs. The intermediate-section data were from a base-case well that used an invert-emulsion drilling fluid, and they were compared with data from other intermediate well intervals in the same field that used an invert-emulsion drilling fluid with the addition of HGSs. Use of HGSs in Drilling Fluids HGSs are inert materials that are used as density-reducing agents. In the oil and gas industry, they are used for reducing the density of drilling fluids and cement blends. These hollow spheres (Fig. 1) are chemically inert, thermally stable (with a softening temperature of 600°C), and made of soda-lime borosilicate with a high strength/density ratio, and they can be added to both oil- and water-based fluids. A density reduction of up to 2.086 lbm/gal is possible when using HGSs. HGS addition helps reduce density and, consequently, the effective circulating density (ECD). Addition of HGSs to a lightweight drilling fluid allows consistent, stable properties and allows for measurement while drilling and controlled management of fluid properties. These lightweight fluids can be used for drilling at balanced, near-balanced, or underbalanced conditions. Applications include depleted reservoirs, geologically fractured formations, poorly consolidated formations, and high-permeability formations. In general, a reduction of differential pressure can result in the elimination of differential sticking, reduction or elimination of fluid loss, and mitigation of formation damage. This can result in higher productivity during drilling operations and during production.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.234
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations5
Published2015
Admission routes1
Has abstractyes

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