Improvement in thermo‐chemical stability of nanocomposite preformed particle gels for conformance control in harsh oil reservoir conditions
Bibliographic record
Abstract
ABSTRACT Gel treatment is one of the most promising technologies to improve conformance control in heterogeneous reservoirs. In this paper, new enhanced preformed particle gels (PPGs) are introduced for conformance control in high‐temperature and high‐salinity oil reservoirs. The thermo‐chemical stability of this product is due to the incorporation of a new monomer, N,N‐dimethyl acrylamide (DA), to the structure of the last well‐known nano‐composite PPGs. Four species, N,N‐dimethyl acrylamide, 2‐acrylamido‐2‐methylpropane sulphonic sodium salt (AMPSNa), acrylamide (AM), and N‐vinylpyrrolidone (NVP) monomers, were used to synthesize these PPGs via free radical cross‐linking polymerization at room temperature using N,N‐methylenebis (acrylamide) as a cross‐linker. The mechanical properties of these new classes of PPGs were enhanced by adding the nano‐clay montmorillonite Na + . A temperature stability agent was also used to make these special PPGs compatible with high temperature and salinity reservoir conditions. A systematic study on PPGs preparation was conducted to improve PPGs stability for harsh reservoir conditions that exist in Persian Gulf oil reservoirs (a temperature of 145 °C and a water total dissolved solid of 225 000 mg/L). PPGs prepared with 0.30 g/g (30 wt%) of a 2:1:1:2 molar mass ratio of AM, DA, NVP, and AMPSNa could be completely stable for the mentioned conditions. Rheological behaviours of these PPGs were investigated and a mathematical model for prediction of storage modulus up to onset of elastic to viscous phase was presented. The results of this study reveal useful insights about the selection of appropriate material for successful conformance control in the heterogeneous reservoirs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".