Reservoir Management of a Low-Salinity Flood on a Per-Pattern Basis
Bibliographic record
Abstract
Abstract We use a streamline-based simulator extended to reactive transport with a comprehensive chemical module to model Low Salinity (LS) flooding. To the best of our knowledge, this is the first study to demonstrate the per-pattern management of LS floods. The new reactive transport simulator with LS capabilities allows assessing the incremental oil recovery on a per-pattern basis as a function of water chemistry, mineralogy, moveable oil in place, and geological uncertainty of each injector pattern. Using a synthetic 3D field example, we show how to quantify the incremental injection efficiency (IIE)—incremental volume of oil produced with respect to standard waterflooding per volume of injected low salinity water—for each pattern. We demonstrate the importance of an uncertainty analysis of cation-exchange capacities and low-salinity oil residual saturations on predicted incremental oil recoveries. Our streamline-based reactive transport approach allows to efficiently explore the dependency of oil recovery on injection water chemistry and reservoir clay mineralogy and provides a unique tool for the management and improvement of LS floods via per-pattern incremental injection efficiencies.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".