Implementation of Amorphous Mesoporous Silica Nanoparticles to formulate a novel water-based drilling fluid
Bibliographic record
Abstract
One of the significant problems of drilling with water-based muds in oil and gas wells is the instability in the Shale Formations. Conventional additives cannot plug nanometer-sized pore throats of Shale. Today, Nanotechnology is used to improve the performance of water-based muds. Adding some Nanoparticles with unique properties to drilling fluids has remarkably improved the mud’s properties. Therefore, this study synthesized Amorphous Mesoporous Silica Nanoparticles (AMSN) with heightened purity and suitable Special Surface Area (SSA) using the sol–gel method. Then AMSN was employed in an eco-friendly water-based mud to investigate the effect on rheological properties, filtration, thermal stability, and Shale recovery. Several analyses were conducted on AMSN synthesized by TEM, FESEM, EDX, DLS, XRF, XRD, BJH, and BET. According to the results, AMSN has an amorphous phase, and its purity, SSA, and total pore volume stand at 98.99%, 226.13 (m2.g−1), and 1.0539 (m3.g−1). Under two conditions, the AMSN was incorporated into the water-based drilling fluid at four concentrations: 0.1, 0.5, 1, and 2% W/W. The primary condition is Before Hot Rolling (BHR), conducted at a temperature of 43.3 °C. A second trial was conducted after hot rolling (AHR) at simulated downhole temperatures of 121.1 °C and 148.8 °C, respectively. In both conditions, the results indicate that the ideal concentration of AMSN is 0.1% by weight and improves the rheological properties, thermal stability, and Shale recovery. Comparatively to base mud, an optimal concentration of AMSN can increase apparent viscosity by 12.5% and reduce fluid loss by 55% in AHR conditions.
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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) | 0.000 | 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 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".