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Record W3094885173 · doi:10.2118/204227-pa

Characterization of the Microstructure of the Cement/Casing Interface Using ESEM and Micro-CT Scan Techniques

2020· article· en· W3094885173 on OpenAlexaff
Xinxiang Yang, Ergün Kuru, Murray K. Gingras, S.S. Iremonger, P. H. Chase, Zichao Lin

Bibliographic record

VenueSPE Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCasingEnvironmental scanning electron microscopeCementMicrostructureMaterials scienceShrinkageCharacterization (materials science)Composite materialPetroleum engineeringScanning electron microscopeGeologyNanotechnology

Abstract

fetched live from OpenAlex

Summary Integrity of the cement/casing interface is an essential element of establishing an effective barrier system in cased and cemented wellbores. Failure to establish and maintain an effective barrier can result in negative environmental and economic impacts, such as leakage of formation fluids into the environment or loss of production and costly remediation. The main focus of this paper is defined as the characterization of the cement/casing interface microstructure, which is critical for better understanding of the requirements for establishing effective zonal isolation and the long-term integrity of cemented wellbore sections of active wells, as well as the integrity of the abandoned wells. The primary objectives of this study were: first, to confirm the most suitable methods for preparing cement/casing samples and characterize the microstructure of the cement/casing interface (i.e., quantify the size of the microchannel due to possible debonding (gap) at the cement/casing interface and its progression along the wellbore axis); and second, to understand how different test methods can affect the microstructure, and once we have a firm baseline, analyze the effect of different cement compositions on the performance. More specifically, we have investigated effects of the cement composition and preparation, environmental conditions [i.e., relative humidity (RH) of the storage and testing conditions], cement shrinkage, and the expansion additive on the integrity of the cement/casing interface at the micrometer scale by using environmental scanning electron microscope (ESEM, 0.05 µm resolution) and microcomputed tomography (μ-CT, 11.92 µm resolution) scanning techniques. Cemented casing samples were prepared by using Class G and special abandonment cement blends with and without expansion additives and stainless-steel pipes. Results showed that any significant change in the RH of the environment during the cement preparation, curing, and testing process significantly affects the size of the gap at the cement/casing interface in test samples. Analyses of the ESEM images have shown that the 2D nonuniform gap size between the cement and the casing is inversely proportional to the change in the RH of the environment. Results suggested that cement slurries set and cured under downhole conditions with relatively constant RH may not undergo significant shrinkage and yield only minimal debonding effect. The 3D gap model reconstructed from the µ-CT images confirmed that the gap between cement and casing mostly occurred at the cement polished surface and the gap didn’t show any significant connectivity below the cement polished surface, indicating that common sample preparation methods can significantly affect the near-surface interface. Cement blends prepared with expansion additives have shown smaller gap size. The use of expansion additives enhances the cement/casing interface integrity by effectively reducing the gap size at the cement/casing interface. We conclude that as long as the RH of the environment does not change significantly, the cement is expected to undergo a limited shrinkage, and the gap between the cement and the casing may not induce any significant leakage pathway because the gap is only locally distributed at the cement polished surface without showing any significant connectivity along the wellbore axis.

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 score0.028
Threshold uncertainty score0.197

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 head0.008
GPT teacher head0.197
Teacher spread0.189 · 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".

Quick stats

Citations25
Published2020
Admission routes1
Has abstractyes

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