Casing Integrity Study for Heavy-Oil Production in Cold Lake
Bibliographic record
Abstract
Abstract The Cold Lake heavy oil development is located in northeast Alberta, Canada. It began commercial operation in 1985 and uses a thermal recovery process called cyclic steam stimulation (CSS). During steaming cycles, the dilation and re-compaction that occur within the reservoir cause the overburden to deform much like the motion of flexing a thick telephone book. At weak overburden layers, shear slip plane(s) can form due to excessive shear stress overcoming the interlayer cohesion. Over multiple steaming/production cycles, the cyclic flexing and associated shear slip may lead to overburden casing fatigue failures. In this paper, a multi-scale geomechanics modeling methodology is presented to predict the onset of failure due to CSS-related ultra low cyclic fatigue (ULCF). The modeling methodology consists of: (i) converting geological data into a representative finite element model of a single or multiple CSS pads, (ii) constructing a near-well submodel that includes thermal cement and casing, and (iii) constructing a detailed casing and connection submodel to predict the ULCF life of a pipe body or connection. To predict the ULCF life of the casing and connection, an algorithm based on the concept of cyclic void growth is incorporated into the submodel. It provides the capability to predict the number of steam cycles to failure using the concepts of demand and capacity. This enables studying the effects of alternative steaming practices on overburden shear slip and casing/connection life. Based on the learning from the multi-scale modeling, it is found that shear displacements on a shear slip plane can be superimposed using a single-well solution. By applying steaming and production scaling functions, the shear slip can be determined at any location and time. Integration of the single-well solution with ULCF algorithm has facilitated development of a new software tool that can be used to manage CSS operations in Cold Lake.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".