Fly Ash-Based Geopolymers as Lower Carbon Footprint Alternatives to Portland Cement for Well Cementing Applications
Bibliographic record
Abstract
Ordinary Portland cement (OPC) is currently the preferred material for the creation of barriers in wells during their construction and abandonment globally. OPC, however, is a very carbon-intensive material with some inherent technical weaknesses. These include a low casing-to-cement bond strength which may allow for the formation of micro-annuli, which in turn can become a conduit for greenhouse gas transport (primarily of methane, a powerful greenhouse gas) to surface. Alkali-activated materials (AAMs), also known as geopolymers, have a much lower manufacturing carbon footprint than OPC and can be a good alternative to OPC for primary and remedial well cementing applications. This paper reports on a comprehensive study into the use of Class F fly ash-based geopolymers for a large variety of downhole well conditions, ranging from lower-temperature surface and intermediate casing cementing conditions to much higher temperature conditions (up to 204 °C (400 °F)) that can be encountered in high-pressure, high-temperature (HPHT) wells and geothermal wells. The rheological and mechanical properties of alkali-activated fly ash with six different sodium and potassium-based hydroxide and silicate activators were measured and compared to OPC. The results show that geopolymer formulation properties can be tuned to a variety of downhole cementing conditions. With the application of a suitable alkaline activator, geopolymers exhibit good compressive and tensile strength and an outstanding casing-to-cement bond strength of up to 8.8 MPa (1283 psi), which is more than an order of magnitude higher than OPC. This has important implications for preventing the creation of micro-annuli as a result of casing-to-cement interface debonding, thereby preventing the potential leakage of methane to the atmosphere on future wells that use geopolymers rather than OPC for barrier creation.
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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) | 0.001 | 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".