Influence of Rigid Side Chains on the Structural Stability of High-Temperature Resistant Fluid Loss Additives for Oil Well Cements: An Experimental Study and Molecular Simulation
Bibliographic record
Abstract
A fluid loss additive (named AAMN) for cementing ultra-deep reservoirs at high temperature (210 °C) was prepared by a free radical copolymerization method using 2-acrylamido-2-methylpropane sulfonic acid (AMPS), acrylamide (AM), acrylic acid (AA), and N-vinyl-2-pyrrolidinone (NVP) as monomers. Filtration tests at 210 °C demonstrated that AAMN reduced the amount of fluid loss by 22% in fresh cement slurry, 23% in 18 wt% NaCl cement slurry, and 16% in 36 wt% NaCl cement slurry compared to AAM (without NVP) when both were added at 6% bwoc ("bwoc" denotes the addition by the weight of the cement). The pore structure was analyzed using the Brenner-Emme-Teller (BET) method and environmental scanning electron microscopy (ESEM), confirming that AAMN could be adsorbed onto the cement particles surfaces, filling the pores and blocking fluid loss channels. The degradation temperatures of the two copolymers were tested by thermogravimetric analysis (TGA-DTGA) and the molecular dynamics behavior of two Amorphous Cell (AC) models (the amorphous structures with randomly arranged Ca2+, Cl−, Na+ and H2O with the AAMN or AAM) built by Materials Studio 7.0 software, were compared: the introduction of five-element NVP side chains didn’t significantly improve the thermal stability of the AAMN relative to the AAM, but resulted in the AAMN molecular chain maintaining a stable geometry at a temperature of 483 K (210 °C). The AAMN also showed more adsorption groups (–SO3− and –COO−) at the high temperatures, which allowed the AAMN to form a dense "network structure" with Ca2+ and H2O molecules, thus effectively blocking the channels for water molecule loss from the cement slurries.
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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".