PERFORMANCE AND PROPERTIES OF KUSP1–BORIC ACID GEL SYSTEM FOR PERMEABILITY MODIFICATION PURPOSES
Bibliographic record
Abstract
Blocking high permeability zones of reservoirs by hydrogels, and diverting the injected fluid towards the unswept zones of the reservoir is a promising method for improving the overall oil recovery in waterflooding and carbon dioxide flooding processes. A polymer gel treatment typically involves the injection of a solution of a medium to high molecular weight polymer and crosslinking agents into the high permeability zones or fractures. The polymer reacts with the crosslinker to form a three-dimensional gel network. KUSP1 biopolymer in sodium hydroxide solution produces a delayed gel system with orthoboric acid. The gelation time varies depending on the concentration of the orthoboric acid and temperature. Syneresis of this gel was studied in bottle tests as well as in the porous media. Samples of KUSP1–boric acid gel lost more than 80% of their initial volume in bottle tests after 250 h. Also, it was observed that this gel, when placed in a sandpack, lost up to about 50% of its initial volume. However, the high values of syneresis did not have a severe effect on the performance of the gel in porous media. KUSP1–boric acid gel was tested for reducing permeability to carbon dioxide and water in a series of tests conducted in a Berea sandstone core. It reduced the carbon dioxide permeability from 164 to 26 md and brine permeability from 420 to 90 md.
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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) | 0.001 | 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 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".