Conformance Control in Oil Reservoirs by Citric Acid-Coated Magnetite Nanoparticles
Bibliographic record
Abstract
Abstract Reservoir conformance control methods may significantly improve enhanced oil recovery technologies through reduced water production and profile correction. Excessive water production in oil and gas reservoirs leads to severe problems. Water shutoff and conformance control are, therefore, financially and environmentally advantageous for the petroleum industry. In this paper, water shutoff performance of citric acid-coated magnetite (CACM) and hematite nanoparticles (NPs) as well as polyacrylamide polymer solution in a heterogeneous and homogeneous two-dimensional micromodel is compared. A facile one-step technique is used to synthesize the CACM NPs. The NPs, which are reusable, easily prepared, and environmentally friendly, are characterized using Fourier-transform infrared spectroscopy, field emission scanning electron microscopy, dynamic light scattering, and X-ray diffraction. The results confirm uniform spherical Fe3O4 NPs of an average diameter of 40 nm, well coated with citric acid. CACM NPs provide a high pressure drop coupled with an acceptable resistance factor and residual resistance factor owing to NP arrangement into a solid-/gel-like structure in the presence of a magnetic field. A resistance factor and a residual resistance factor of 3.5 and 2.14, respectively, were achieved for heavy oil and the heterogeneous micromodel. This structure contributed to an appreciable plugging efficiency. CACM NPs respond to ∼1000 G of magnetic field intensity and display a constant resistance factor at intensities between 4500 and 6000 G. CACM NPs act as a gel, forming a solid-/gel-like structure, which moves toward the magnetic field and thereby shuts off the produced water and increases the oil fraction. The findings of this study suggest the ability to shut off water production using specially designed magnetic field-responsive smart fluids. The application would require innovative design of field equipment.
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) | 0.000 | 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".