Influence of permeability and strength of bentonite-based and low-carbon-based grouts on long-term wellbore integrity and sealing
Bibliographic record
Abstract
Wellbores are used to provide underground access for fluid injection, extraction, and storage. The long-term exposure of wellbores to in-situ conditions can potentially weaken their mechanical integrity and result in geo-hazards. Also, it is important to achieve adequate wellbore sealing to prevent the inter-annular communication of fluids and subsequent underground leakages. Hence, cement-based grouts (e.g., portland limestone cement) and bentonite are critical to underground construction and wellbore installations. Bentonite has been reported to have sealing potential in underground engineering infrastructure due to its inherent swelling property to fill porous zones within a rock-wellbore interface. However, the integrity and resilience of these cement-based grouts in wellbore sealing and plugging under in-situ stresses and their ability to provide sufficient zonal isolation in the long term are unknown. In this study, we investigated the hydraulic (permeability) and mechanical (uniaxial compressive strength, UCS ) properties of low-carbon-based and bentonite-based grouts under in-situ stress conditions, and further assessed their long-term integrity based on plugging and sealing performances. To address these, we considered four (4) cement-based grouts with or without bentonite in their mixture (Type 1, T1; Type 1 L, T1L; Type 1 + bentonite, T1B; and Type 1 L + bentonite, T1LB). Next, we conducted permeability and uniaxial compression tests on grout specimens. The results indicate that for wellbore integrity, cement grout containing bentonite may provide the highest percentage decrease in permeability (-85 % to −95 %) and the highest percentage increase in UCS (+116 % to +130 %) relative to the cement grouts without bentonite. However, assessment of these cement grouts indicated that T1 (with the second lowest permeability and second highest UCS ) can provide the best seal and wellbore reinforcement. Further, with optimum mixture designs, cement-based grouts can provide an effective seal and ensure long-term wellbore integrity if the water-to-cement ratio is reduced. The findings from this study will contribute to the state of knowledge in underground engineering for more efficient sealing of the wellbore annulus, thereby supporting the long-term durability of deep underground infrastructure and geosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".