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Record W2065959363 · doi:10.2118/2004-090

Use of Cement as Lost-Circulation Material: Best Practices

2004· article· en· W2065959363 on OpenAlexaff
EG Fidan, Tayfun Babadagli, Ergün Kuru

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCementLost circulationCirculation (fluid dynamics)Computer sciencePetroleum engineeringMaterials scienceGeologyEngineeringMechanical engineeringComposite materialAerospace engineering

Abstract

fetched live from OpenAlex

Abstract Fluid loss is encountered in almost all drilling operations. Depending on the severity of lost circulation and the size and type of the thief zone, different lost-circulation materials (LCMs) are used for curing. Cement is one of the most common LCMs. Various types of cement have been used as LCMs in the past. Recent developments in cement technology and the understanding of lost circulation have produced customdesigned applications utilizing effective cement types and compositions. Applications also vary depending on the drilling fluid type and its properties. Custom-designed applications include thixotropic and ultrathixotropic cement slurries; slurries containing cello flakes, mica, and CaCO3 for mechanical bridging; unique spacers and surfactant packages; and foamed cement for controlling loss. Selection of proper cement type and injection procedure calls for specific information such as formation properties, wellbore conditions, and thief zone characteristics. Laboratory experiments are recommended in this process. Field observations are also critical in making the final decision for selecting the optimum treatment fluid train and application strategy. This paper discusses the application of various cement types (and process designs) as LCMs. Solutions to problematic field cases are provided. The criterion for selecting the best cement compositions is outlined. Optimal strategies are also presented. Introduction Lost circulation is the partial or complete loss of drilling fluid or cement during drilling, circulation, running casing, or cementing operations. According to a 1991 API survey, lost circulation occurs during drilling on approximately 20 to 25% of wells drilled worldwide.1 Loss of drilling fluid can result in increased cost, loss of time, plugging of potentially productive zones, blowouts from decreased hydrostatic pressure in formations other than the thief zone, excessive inflow of water, and excessive caving of the formation. Therefore, application of an immediate solution to lost circulation is an essential part of drilling engineering. Lost circulation occurs through existing high-permeability zones such as highly fractured, vuggy, or cavernous structures or induced fractures when the hydrostatic pressure of drilling fluid exceeds the breaking strength of the formation. Carbonates are good examples of the former case, while the latter types typically occur in sandstones. For lost circulation to occur, the size of the pore openings of the included fractures must be larger than the size of the solid particles in the drilling fluid. Lost circulation in naturally fractured, cavernous/vugular and unconsolidated formations cannot be avoided completely. The following preventive measures can be used however in other cases:1set casing to protect weak formations,maintain a minimum safe drilling fluid density,avoid excessive downhole pressures caused by improper drilling fluid rheology, hydraulics, high flow rate, thick filter cakes, surge pressures during tripping in the well, bridges occurring in the annulus, and high shut-in surface pressures. Problems that emerge due to lost circulation in the early and later stages of the drilling operation are different. For example, in surface holes, lost circulation has been known to cause massive washouts that in extreme cases, lead to the loss of the drilling rig.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.053
GPT teacher head0.270
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations48
Published2004
Admission routes1
Has abstractyes

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