Numerical simulation of the thermomechanical behavior of cement sheath in wells subjected to steam injection
Bibliographic record
Abstract
The exposure of oil wells to extreme conditions may lead to numerous problems associated with the loss of integrity of the cement sheath. A damaged cement sheath may result in fluid leakage and in some cases cause significant economic loss and environmental degradation. Thermal recovery processes are common techniques used to enhance oil recovery by submitting the materials to severe temperature gradients. However, this heating process induces thermomechanical stresses in the cement sheath which may lead to its cracking. This paper investigates the effects of cement slurry formulation on the thermomechanical behavior of cement sheath in wells subjected to the first heating phase of steam injection. For this purpose, three types of cement slurry formulations, one standard and two others with latex additions, were designed, mixed and tested in the laboratory to evaluate their mechanical properties. An axisymmetric finite element model was then developed and used to conduct a parametric study to investigate the thermomechanical response of oil wells with cement sheaths made of the various cement slurries, as well as the influence of applied temperature gradients, heating durations and formation stiffness. The results mainly show that issues of cement sheath integrity under steam injection are generally localized in the region near the formation, and that they are closely related to the heating phase of the well. It is also found that combining the use of a more flexible and expansive cement slurry formulation with the slow application of temperature gradient generally improves cement sheath integrity during steam injection. These beneficial effects were shown to be more effective for stiffer rock formation and larger applied temperature gradients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".