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Record W4407895088 · doi:10.2118/223667-ms

Experimental Investigation of Penetration and Plugging Performance of Cement Blends Used for Squeeze Cementing in Sub 150 Micron Gaps

2025· article· en· W4407895088 on OpenAlexaff
Muhammad A. Thaika, Ke Hu, Ergün Kuru, S.S. Iremonger, Huazhou Li, Zichao Lin, Gunnar DeBruijn

Bibliographic record

VenueSPE/IADC International Drilling Conference and Exhibition · 2025
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsAlberta EnergyUniversity of Alberta
Fundersnot available
KeywordsCementMaterials scienceComposite materialPortland cementPenetration (warfare)ComminutionSlurryMetallurgy

Abstract

fetched live from OpenAlex

Abstract Squeeze cementing is one of the most common techniques used to remediate well integrity issues. Understanding the mechanisms controlling cement particle penetration and/or bridging as well as assessing the potential of cement blends in narrow gaps are crucial for the design of a successful squeeze cement job. The main objectives of this study are to determine the penetration potential of different cement blends; determine the critical gap width where bridging of cement particles starts and to compare the fracture conductivity of the sample before and after remediation. Four different cement systems were tested for their ability to penetrate and plug narrow fracture gaps, including class G (A1) (largest particles), Portland limestone blended cement (A2) (medium particles), microfine cement (A3) (smallest particles), and Semi-microfine Class C (A4). Cylindrical cement plugs were cured at 1000 psi and 50 °C for 7 days and cut into halves to create replicas of fractured cement samples. Metal shims of 150, 100, and 50 microns in thickness were used to control the gap size. Cement micro squeezes jobs were conducted using a conventional core flow set-up. The fractured cement samples were imaged using micro-CT scanning technique before and after remediation to determine both the fracture width and the slurry flow pathway. A significant reduction (varying between three to six order of magnitudes) of the fracture conductivity was observed because of squeeze cementing, even though the cement did not fully penetrate the fractured cement samples in some cases. Squeeze cement slurry progression in the fracture takes place in several modes, all controlled mainly by the narrowest gap width size/particle size ratio (GW/PS ratio). At high GW/PS ratio, the cement slurry was distributed uniformly. At moderated GW/PS ratio, bridging and partial plugging of the fractures were observed, leading to slurry flow in fingered pattern. At low GW/PS ratio only filtrate flow was observed. Comparison of the results from all 13 cases of squeeze cementing experiments conducted throughout this study suggest that there may be a critical gap width/particle size (D90) ratio, which controls the particle bridging versus flow (and the depth of cement slurry penetration into the fracture) and this number is somewhere between 2.2 and 2.4. When the minimum fracture gap width/particle size (D90) ratio is around 2.2 and lower, cement slurry starts to bridge, thus preventing further filling of the gap by the squeezed cement. When the minimum fracture gap width/particle size (D90) ratio is sufficiently higher than 2.2, cement bridging does not occur, and the cement slurry completely fills the fracture. When the minimum fracture gap width/particle size (D90) ratio is much less than 2, the cement slurry flow stops suddenly in the narrowest region without any fingering and filtration. Preliminary results from this study suggest that remedial cementing penetration strongly correlates with fracture gap size/cement particle size (D90) ratio. However, more data, like the ones presented in this study, are needed before we can suggest a more accurate value of critical gap width/particle size ratio, which controls the probability of plugging in any squeeze job.

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 score0.371
Threshold uncertainty score0.513

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 head0.012
GPT teacher head0.230
Teacher spread0.218 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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