Self-Healing Cement—Novel Technology to Achieve Leak-Free Wells
Bibliographic record
Abstract
Abstract There is a very large number of wells worldwide that leak or have sustained casing pressure (SCP). In Central Europe and the Middle East there are hundreds of wells with reports of trapped pressure that cannot be bled off. In the US and Canada there are thousands of wells leaking to surface, which may or may not be discharged to the atmosphere. Furthermore, 25% of all wells in the Gulf of Mexico have measurable sustained casing pressure. Additionally, remedial work fixing issues relating to cement failure has been estimated to be more than $50M a year in the US alone. Throughout the lifecycle of a well, planned cycle or operational changes can contribute to unknown damage to the cement sheath integrity that is hard to identify or locate, including the generation of a microannulus. Within flow paths, hydrocarbons can either migrate to surface, or become trapped below the wellhead leading to pressure build-up. Typical events occur during cementing, while perforating or stimulating, throughout the subsequent production, and even after abandonment. These can easily create this loss of cement integrity. This paper describes a novel isolation system that is activated only when a cement integrity problem occurs. The system will automatically and rapidly form a complete hydraulic barrier by swelling in the presence of hydrocarbon flow. Once activated, it will seal the damaged zone, and will even be able to be activated again, should further damage occur again during production or abandonment. The system has properties equivalent to conventional cement systems, and requires no modifications to standard surface equipment. High pressure static and dynamic laboratory tests highlight the ability of the system to rapidly shut off gas flows within 30 minutes. Field tests have also highlighted the robustness of the system, with a number of wells currently using the system remaining leak-free.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".