New Attempt in Improving Sweep Efficiency at the Mature Koluel Kaike and Piedra Clavada Waterflooding Projects of the S. Jorge Basin in Argentina
Bibliographic record
Abstract
Proposal Most of the mature waterflood projects at the San Jorge Basin have been affected by two main problems, poor displacement and sweep efficiencies, both have limited the recovery factor achievable. This paper presents a field trial of a new reactive particulate system in an attempt to improve this volumetric sweep efficiency. This particulate system is being used to treat selected injectors operated by Pan American Energy in the Koluel Kaike and Piedra Clavada fields, located in the southern part of Argentina. The main purpose of this field trial is to demonstrate the ability of the new system to improve oil recovery by diversion of injected water into the poorly swept zones around the thief zone or streaks. This state-of-the-art technology is able to propagate deep into thief zones and has a novel mechanism of action to overcome the observed limitations in conventional polymer flooding and gel processes. The particles are manufactured having properties which allow it to propagate through porous media with the injection water. Once in the reservoir and under the influence of heat the particle expands to a size that can block pore throats, so water injected after treatment is diverted into less efficiently swept zones. The paper will describe in detail the mechanism and selection criteria of reservoirs and wells to apply the mentioned IOR technology. Theoretical and practical issues involved at the design of the application, as well as operational and logistics aspects will be also included. Finally, it will include the updated information available from the ongoing pilot tests.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".