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Record W4415585758 · doi:10.2118/223667-pa

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

2025· article· en· W4415585758 on OpenAlexaff
Muhammad A. Thaika, Ke Hu, Ergün Kuru, S.S. Iremonger, Huazhou Li, Zichao Lin, Gunnar DeBruijn

Bibliographic record

VenueSPE Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsAlberta EnergyUniversity of Alberta
Fundersnot available
KeywordsCementPortland cementPenetration (warfare)Spark plugComminutionOil well

Abstract

fetched live from OpenAlex

Summary 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 is important 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 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 American Petroleum Institute (API) Class G (A1, largest particles), Portland limestone-blended cement (A2, medium particles), microfine cement (A3, smallest particles), and semi-microfine API Class C (A4). Cylindrical cement plugs were cured at 6890 kPa and 50°C for 7 days and cut into halves to create replicas of fractured cement samples. Metal shims of 150 μm, 100 μm, and 50 μm in thickness were used to control the gap size. Cement microsqueeze jobs were conducted under a differential pressure of 2068 kPa (300 psi) using a conventional core flow setup. The fractured cement samples were imaged using microcomputed tomography (CT) scanning before and after remediation to determine both the fracture width and the slurry flow pathway. Results showed that A2, A3, and A4 cement were able to fully penetrate and seal fractures ≥70–80 μm, whereas A1 experienced bridging and nonuniform penetration. For narrower gaps (around 40 μm), none of the tested cement blends achieved complete penetration. A reduction (varying between three and six orders of magnitude) of the fracture conductivity was observed after 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 (GW)/particle size (PS) ratio. At a high GW/PS ratio, the cement slurry was distributed uniformly. At a moderate GW/PS ratio, bridging and partial plugging of the fractures were observed, leading to slurry flow in a 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 suggests that there may be a critical GW/PS (D90) ratio, which controls the particle bridging vs. 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 GW/PS (D90) ratio is around 2.2 or lower, cement slurry starts to bridge, thus preventing further filling of the gap by the squeezed cement. When the minimum fracture GW/PS (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 GW/PS (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 those presented in this study, are needed before we can suggest a more accurate value of critical GW/PS 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.271

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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