Silicate-Activated Geopolymer Alternatives to Portland Cement for Thermal Well Integrity
Bibliographic record
Abstract
Abstract Ordinary Portland cement (OPC) has been the material of choice for oil & gas well cementing and abandonment for many decades now. However, there are drawbacks to the use of OPC for cementing and abandonment purposes, particularly in wells with higher temperatures. OPC is brittle and does not re-heal when cracked. It is easily contaminated by mud and spacer fluids. Furthermore, it has relatively low tensile strength and low strength when bonding to rock formations and casing. Moreover, the production of OPC is the 2nd largest source of CO2 emissions in the world. At the CODA industry-affiliate consortium at the University of Texas at Austin dedicated to well construction, decommissioning and abandonment, work is ongoing to find technically superior alternatives to OPC. Particularly promising materials are so-called geopolymers, formed by activating an alumino-silicate material such as fly ash (a waste material that is often discarded) with an alkali. It was found that these geopolymer materials offer more ductile strength and failure behavior, considerable resistance to contamination, higher tensile strength and bond strength, and an ability to re-heal when damaged. The results obtained for geopolymers formed by activating flyash with potassium and sodium silicates indicate that these may be well-suited for achieving long-term thermal well integrity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".