Effective Conformance Control Strategies in Mature Waterfloods with Comingled Injection
Bibliographic record
Abstract
Abstract The Sparky/General-Petroleum/Rex/Lloydminster formations are subject to a mature waterflood (1300+ vertical/horizontal wells) in Chauvin, Alberta, where permeabilities and viscosities range from 10 to 200 mD and 40 to 150 cp, respectively, and adverse mobility ratio, injection-induced fracturing, and thief zones trigger water channeling. Conformance polymer-gel squeeze is common for alleviating channeling. With sufficient gel (10-25% of channel volume) selectively placed into water-bearing channels, injected water is effectively redistributed to improve sweep efficiency and pressure support. Selective gel squeeze with diverter entails injecting a diverter slug ahead of self-crosslinking gels to divert gels to high-permeability water-bearing channels, avoiding diversion into lower-permeability and/or oil-bearing pores. Selective gel squeezes are complex, costly, and erode profit margins in mature waterfloods. In simpler-to-execute, more-economical non-selective gel squeezes (without diverters), bullhead gel treatments are ‘designed’ to enter higher-permeability zones. In this work, effective, economical conformance control strategies were developed using non-selective gel squeezes. Hall/Chan/Conformance/Heterogeneity-Index Plots were used to identify candidates (26 injectors supporting 300+ producers) and channel volumes. Gel strength of crosslinked acrylamide-polymer (4000-15000 PPM concentrations) was monitored using Sydansk grading. During the treatment, offsets were monitored for polymer breakthrough using flocculation tests. Any offending producer was shut in until resuming water injection. After treatment, water post-flush displaced the gel, and injector was shut in for 1-4 weeks (longer shut-in for lower polymer concentration) to allow gel to set before resuming water injection. Non-selective bullhead gel with low-to-medium strength, lower treatment rate, and extended water post-flush triggers both near-wellbore diversion and protection against crossflow back into offending thief zones deeper in the reservoir, resulting in 40% costs reduction and up to 50% oil rates improvement with lower treatment volumes (5-10% of channel volume). Sufficient low-to-medium strength gel is required for deeper gel placement in the reservoir. Injecting gel in stages of increasing polymer concentration ensures that lower polymer-concentration gels at the leading edge of the treatment occupy rock furthest from the wellbore where they will not require as much strength to resist lower differential pressure to which they will be exposed. Conversely, higher polymer-concentration gels injected at the end of the treatment occupy rock nearest the wellbore where more strength is required to resist higher differential pressure. Furthermore, injecting gel at lower rates ensures that it remains selective to higher-permeability, more-conductive pores (i.e., water flow paths), minimizing gel diversion into oil-bearing pores. Shut-off candidates exhibit out-of-zone injection to adjacent formation(s) because of crossflow. An extended water post-flush pushes high-strength gel deep in the formation to mitigate crossflow. Economical conformance control protocols were developed to selectively heal high-permeability water-bearing channels in comingled injectors using low-rate, low-volume gel squeezes with low-to-medium strength, eliminating the need for diverters and workover rig.
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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) | 0.000 | 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 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".