Evaluation of Primary Cementing Operations for a Carbon Capture Storage (CCS) Well: A Case Study
Bibliographic record
Abstract
Abstract In this case study, we explore a carbon capture storage (CCS) well that is typical of those currently cemented in Alberta, Canada. Storage of carbon dioxide (CO2) deep underground in a safe geological structure, involves the injection of CO2 into well and trapping of CO2 within the formation, without leakage. A key factor contributing to limiting any risk of gas leakage in CCS is a successful primary cementing operation. To retain primary control of the well, the downhole pressure should be less than the fracture pressure and higher than the pore pressure. We use a one-dimensional (1D) hydraulic simulator, to pressure match with recorded pump pressures. While the results illustrate that primary well control is maintained throughout, the pressure comparison to the acquisition data reveals significant differences. This might be due to an increased hole size, due to ellipticity or a washout. Fluid contamination during the annular displacement flow might also alter fluid rheological properties, leading to a higher simulated pump pressure than to the operation data. Secondly, we use a two-dimensional gap-averaged model (2DGA) to determine the positioning of the fluid sequence at the final time of the job. This gives us insights into potential mixing and fluid contamination, possible generation of residual mud wall layers and mud channels at any given depth. The simulated cement job outcomes show a mud channel forming on the narrow side of the annulus. Adding centralizers to the deviated part of the well can enhance the efficiency of the displacement process and reduce the likelihood of mud channel formation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".