A case study for tailored formulation of geopolymers aided by annular displacement simulations
Bibliographic record
Abstract
The substitution of Ordinary Portland Cement (OPC) by geopolymer materials for sealing oil and gas wells has the potential to reduce the associated carbon footprint and provide more flexibility and durability at downhole conditions compared to OPC. However, geopolymer materials have chemical incompatibilities when mixed with those drilling muds commonly used. Thus, careful use of spacers is needed. In this work, we present a case study that explores the process of designing compatible spacers for sealing a wellbore with a geopolymer. To ensure negligible mud-geopolymer contamination, the spacer design is backed-up by the results of 2D-gap averaged simulations of annular displacements. Simulation results are post-processed into maps of displacement efficiency for the cementing operation. The results show a broad operating window of eccentricities, density, and rheology for an effective spacer design, i.e. producing near-perfect displacement of the bulk fluids. While qualitatively the results conform to best practices (high standoff, positive density, and rheology hierarchies), the use of simulation allows for quantitative prediction. This highlights the benefits of using 2D flow simulations, in particular reducing the risk of deployment of new materials.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".