Effect of Dynamic Loading on Wellbore Leakage for the Wabamun Area CO2-Sequestration Project
Bibliographic record
Abstract
Summary The most viable options for permanent removal of carbon dioxide (CO2) from the atmosphere include large-scale injection of CO2 from stationary sources, such as coal-fired power plants and heavy-oil production, into brine-filled formations. One of the main risks identified with storing CO2 in the subsurface is the potential for leakage through existing wells penetrating the caprock. The wellbore system has several components that can fail and create leakage pathways, including type and placement of wellbore casing and cements, completion method, abandonment, and wellbore expansion or contraction by changes in temperature and pressure. Of the 1,000 wells in the study area near Wabamun Lake, Alberta, 95 wells penetrated the immediate caprock above the proposed Nisku injection formation and were identified as potential leakage pathways. The leakage risk of these wells was evaluated on the basis of knowledge of the well design, current well status, and historical regulations in the area. Only four wells, for the subset of 27 wells studied, were identified as requiring workover, which was less of a problem than anticipated. To evaluate the risk of creating leakage pathways by thermal and pressure changes caused by CO2 injection, a 3D finite-element model was built by use of poroelastoplastic material models for cement and formation. Multistage simulations for casing/cement and cement/ formation interactions with temperature-enabled elements were conducted. A parametric study of cement properties was conducted to investigate cement design and its mechanical properties for injection wells. The simulation results indicated that thermal cooling might reduce near-wellbore stresses, which would increase the risk of integrity loss in casing/cement and cement/formation. The parametric study revealed that the risk of debonding and tensile failure would increase with increasing Young’s modulus and Poisson’s ratio of the cement under dynamic-loading conditions. In addition, low mechanical cement strength would increase the risk of shear failure in the cement.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".