A Magnetic Resonance Study of Low Salinity Waterflooding for Enhanced Oil Recovery
Bibliographic record
Abstract
Low salinity waterflooding (LSF) has been proposed to improve oil recovery, with major projects in progress worldwide. There is however no consensus on the mechanisms of LSF for enhanced oil recovery (EOR). Wettability change is the most widely accepted mechanism. In this work, magnetic resonance (MR) and magnetic resonance imaging (MRI) were employed to monitor oil displacement processes during model laboratory scale LSF experiments. The MR and MRI measurements permit evaluation of putative LSF mechanisms. Two clay-coated sand packs, one with nonswelling kaolinite, the other with swelling montmorillonite, were prepared as model porous media for LSF. The interactions between pore fluids (oil and water) and the clay-coated pore surfaces were evaluated with relaxation time measurements. A MRI methodology, spin echo single point imaging (SE-SPI), was employed to spatially resolve the T 2 distribution along the sand pack. The oil saturation profiles were determined from SE-SPI measurements. A new differential relaxation time distribution method is proposed in this work for oil saturation estimation. The pore fluid self-diffusion coefficients were measured. The mechanism of wettability change for LSF is suggested on the basis of the oil diffusion coefficient variation with LSF. The similarities and differences between the kaolinite and montmorillonite behaviors are discussed. This work demonstrates that MR and MRI are robust tools to monitor oil displacement processes, with the potential to reveal the mechanisms of LSF and other procedures for enhanced oil recovery.
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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".