Application of Chemical Tracers in IOR: A Case History
Bibliographic record
Abstract
Abstract Secondary recovery is a process in which reservoir fluid is mobilized and moved from an injection well toward a production well. The success of this process greatly depends on the knowledge of reservoir continuity and uniformity, in terms of fluid transmissibility, and how much of the reservoir fluid volume can be contacted by the injection fluid. In any water/gas flood injection project, fluid channeling through mini-fractures, faults, and high permeability streaks results in problems such as poor reservoir sweep efficiency and low hydrocarbon recovery. Therefore, knowledge of direct communication between the injection and production wells as well as an understanding of formation heterogeneity can be of great help to overcome these problems. While techniques such as seismic, mapping geological deposition and reservoir simulation provide valuable information about the feasibility of secondary recovery projects, tracer testing is the only available method that provides valuable information on direct communication, flow-path, and formation heterogeneity across the injection and production wells. This paper presents a detailed review of chemical tracer applications in IOR with a supportive case history from a water-flood field. The paper also presents interpretation and discussion of the results on direct communication identification, formation heterogeneity evaluation, and swept pore volume calculation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".