A Singular Methodology to Design Cement Sheath Integrity Exposed to Steam Stimulation
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Bibliographic record
Abstract
Abstract Heavy oil production is one of the new challenges the Oil and Gas industry faces today with reserves of trillions of barrels. When well production is considered, heavy oils viscosity must be reduced to gain mobility and have oil flowing. Between all possible techniques, steam stimulation is today the most promising. Steam is injected through the well down to the reservoir to warm it up with temperature up to 250°C, inducing extreme thermal stresses to the well, especially when the temperature gradient through the well components is maximum, like during first steam injection or during work-over when the well should be quickly cooled down and heated up again to rapidly restart production. To avoid zonal isolation failure and steam release at surface, new rules should be considered when designing barriers of a well exposed to steam stimulation, especially in very shallow fields. If classic rules could be applied for casing design, pioneering rules should be considered to design cement sheath. This paper presents a singular methodology to design cement sheath long term integrity in very shallow conditions. First, stresses in the field are evaluated with a rock mechanics simulation. Then, thermal gradient in well components while heating are evaluated with a thermal simulation of the well. Both results are injected in Total's dedicated software to determine the mechanical properties the cement should have to withstand thermal stresses. Different cement systems from service companies are then triaxial tested before being evaluated in a cell reproducing the field to validate simulations' results. This methodology was successfully applied to Joslyn field wells, Canada, where a resilient cement system with low Young's modulus and high tensile strength was selected and pumped. Since results are positive, this methodology has been extended to other Total's fields worldwide where steam simulation is considered.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it