Development of Nanofluids for the Inhibition of Formation Damage Caused by Fines Migration: Effect of the Interaction of Quaternary Amine (CTAB) and MgO Nanoparticles
Bibliographic record
Abstract
Fines migration is a common problem in the oil and gas industry that causes a decrease in productivity. In this sense, the main objective of this study is to develop nanocomposites based on the interaction of quaternary amine (hexadecyltrimethylammonium bromide—CTAB) and MgO to enhance the capacity of retention of fine particles in the porous medium. MgO nanoparticles were synthesized by the sol–gel method using Mg(NO3)2·6H2O as a precursor. Nanoparticles were characterized by dynamic light scattering (DLS), the point of zero charge (pHpzc), thermogravimetric analysis, and Fourier transform infrared spectroscopy (FT-IR). Different nanoparticle sizes of 11.4, 42.8, and 86.2 nm were obtained, which were used for preparing two system nanofluids. These systems were evaluated in the inhibition of fines migration: in the system I MgO nanoparticles were dispersed in a CTAB-containing aqueous solution, and system II consists of a nanocomposite of CTAB adsorbed onto MgO nanoparticles. The fines retention tests were performed using Ottawa sand 20/40 packed beds and fine particles suspensions at concentrations of 0.2% in a mass fraction in deionized water. Individual and combined effects of nanoparticles and CTAB were evaluated in different treatment dosages. The analysis of the interactions between the CTAB and the MgO nanoparticles was carried out through batch-mode adsorption and desorption tests. The best treatment in the system I was selected according to the fines retention capacity and optimized through a simplex-centroid mixture design for mass fractions from 0.0% to 2.0% of both CTAB and MgO nanoparticles. This statistical analysis shows that the optimal concentration of these components is reached for a mass fraction of 0.73% of MgO nanoparticles and 0.74% in mass fraction of CTAB, where the retention capacity of the porous medium increases from 0.02 to 0.39 mg·L−1. Based on the experimental results, the nanofluids combining both components showed higher retention of fines than the systems treated only with CTAB or with MgO nanoparticles, with efficiencies up to 400% higher in the system I and higher up to 600% in the system II. To evaluate the best performance treatment under reservoir conditions, there were developed core flooding tests at fixed overburden pressure of 34.5 MPa, pore pressure at 6.9 MPa and system temperature at 93 °C. Obtaining critical rate increases in 142.8%, and 144.4% for water and oil flow in the presence of the nanofluid. In this sense, this work offers a new alternative for the injection of nanocomposites as a treatment for the problem of fines migration to optimize the productivity of oil and gas wells.
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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".