Synthesis and preparation of poly (AM-co-AMPS)/GO nanocomposites hydrogel as a rheology modifier and fluid loss controller for use in oil well cementing
Bibliographic record
Abstract
The aim of the present work is to evaluate the influence of graphene oxide (GO) on copolymer nanocomposites hydrogel structure based on acrylamide (AM) and 2-acryloamido-2-methylpropane sulfonic acid (AMPS) in the presence of the N, N-methylene-bis-acryl-amide (NMBA) by the free radical copolymerization technique. The influence of poly (AM-co-AMPS)/GO as a rheology modifier along with fluid loss controller on the rheological and mechanical properties of brine cement slurry was also investigated. The characterization results confirmed the synthesis of AM monomer and AMPS and showed their grafting with GO surface. The poly (AM-co-AMPS)/GO hydrogel improved the rheological properties of the cement slurry as compared to conventional cement slurry. Furthermore, free water and fluid loss of cement slurry declined by adding the copolymer nanocomposites hydrogel at elevated temperatures. Moreover, not only the thickening time of cement slurry dropped but also, the compressive strength increased with a rise of nanocomposites hydrogel concentrations. However, the nanocomposite hydrogels showed a great effect on early compressive strength than the final compressive strength. The results of this investigation revealed the excellent performance of crosslinked structure of copolymer nanocomposites hydrogel due to the linking of sulfonated AMPS and AM on the surface of GO nanosheet in the attendance of NMBA. This renders greater stability to the cement slurry against salinity and temperature changing of well formations during cementing operation. Thus, poly (AM-co-AMPS)/GO as a suitable rheology modifier and property enhancer can be applied in oil well cementing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".