Quantifying 2D and 3D Fracture Leakage Pathways Observed in Wellbore Cement after Uniaxial Compressive Loading
Bibliographic record
Abstract
Abstract Stress-induced fractures in wellbore cement can form high-risk pathways for methane or carbon dioxide leakage yet little to no quantitative information on the impact of these fractures has been reported. To investigate this, scanning electron microscopy (SEM) and micro computed tomography (micro-CT) techniques were utilized to quantify the 2D and 3D geometrical parameters of cement fractures in mature thermal thixotropic cement samples that were subjected to pre- and post-peak compressive stress. A novel simulation method was also proposed to quantify the impact of the stress-induced realistic 3D fractures on the cement permeability. Results show that: i-) For pre-peak samples, 90% of the 2D fractures have length and width smaller than 100 μm and 5 μm, respectively. Although higher compressive stress reshaped the 3D fractures and increased the fracture length and width, no well-propagated fractures were observed; ii-) For post-peak samples, distinctly visible (> 0.1 mm) well-propagated fractures were generated but failed to penetrate the entire sample, therefore the effect of stress-induced fractures (up to 1.0% strain) on cement sample's permeability is limited; and iii-) CT-based 3D visualization and simulation both show that inclusion of a correctly engineered fiber additive is able to blunt the fracture propagation in cement samples. We conclude that up to the uniaxial compressive strength, the monotonic compressive stress is not likely to create leakage pathways in wellbore cement since the 2D fractures observed in SEM images are in limited dimensions and the large 3D fractures characterized in CT images have poor connectivity. Inclusion of a fiber additive is expected to enhance cement integrity by limiting the fracture propagation.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".