Core Floods vs. Field Pilot – Effectiveness of Microemulsions in Conventional and Unconventional Waterfloods
Bibliographic record
Abstract
Abstract Application of chemistries for waterflooding has traditionally required a significant upfront investment in core flood testing. Investments of this sort equate to money and time spent on a reservoir screening tool which does not guarantee an accurate translation into pilots. The aim of this paper is to explore core flood results in conjunction with pilot results for conventional and unconventional reservoirs where microemulsions are deployed in order to enhance oil recovery. Microemulsions act as a delivery platform for solvent (terpene) and surfactant mixtures throughout a given rock volume. Their ability to alleviate damage and change the energetics of surfaces is believed to enhance mobilization of oil. They’re optimized for a given reservoir in the laboratory based on fluid-fluid and fluid-rock interactions. This includes adsorption (persistency), asphaltene wash-off, demulsification, drop size, and interfacial tension testing. We in turn label changes in injectivity of water as well as increases in oil production as indicators of success in core floods and pilots. The above strategy has led to microemulsion optimization in Taylorton Bakken (which is more conventional) and Lower Shaunavon (which is more unconventional) in SE and SW Saskatchewan, Canada. These are characterized by changes in permeability, temperature, mineralogy (quartz vs calcite), oil (paraffinic vs asphaltenic) and water (high vs low salinity). This study demonstrates a beneficial core flood and pilot response in conventional reservoirs using microemulsions. What’s however interesting and noteworthy is that the core flood response is negligible in unconventionals (<5% incremental oil recovery) due in part to asphaltenes plating out on the core’s exterior surface during restoration of wettability, whereas the pilot response is quite positive. The major highlight of this work is the need to address the discrepancy in core flood testing and pilot results in unconventional reservoirs. This is required before core flood testing can be used as a reliable screening tool for unconventional reservoirs. We’ve furthermore demonstrated the beneficial impact of microemulsions in both conventional and unconventional reservoirs as well as the need for optimization based on fluid-fluid and fluid-rock interactions.
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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".