Understanding Flow Through Interwell Tracers
Bibliographic record
Abstract
Abstract This paper presents the successful implementation of an interwell tracer program performed in a multilayered reservoir with mature waterflooding. The objectives of the program are to evaluate water channeling in a high water cut field and, assess sweep efficiency improvements in an EOR pilot test. An extensive methodology was prepared to ensure the quality of the program. The first stage followed the screening of areas and reservoir layers with underperforming waterflooding. After that, the design stage included a selection of tracer available in the market, volume calculation and breakthrough time simulations. The execution plan defined the optimum injection rates, equipment and lab tests requirements for the monitoring and field sampling schedule. The results were being recorded in a monthly progress report and analyzed by a technical team in charge of the project. Interwell tracers made possible to confirm water channeling issues and provided information about the severity of preferential flow using the breakthrough times obtained for each reservoir layer. It was also possible to observe how changes in injection rate impacted the recovery of the tracers, creating different flow patterns at different flow rates. The six-month monitoring ended with the estimation of channel volume to design water conformance treatments. In addition, the findings from the interwell tracer conducted in the EOR pilot test showed an increase in the breakthrough time after three years of polymer flooding and, a change in flow patterns that allowed the displacement of previously bypassed oil. These results are interpreted as a measurable improvement of sweep efficiency and served as input to appraise the performance of the pilot test.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".