Designing a Robust ASP Formulation for The Kuparuk Field
Bibliographic record
Abstract
Abstract This paper discusses surfactant, co-solvent, alkali and polymer (ASP) formulations developed for the Kuparuk Field in Alaska. This field is a mature conventional reservoir that exhibits favorable characteristics for surfactant flooding. The formulations have been tested in the laboratory and in the field with good results. In core floods with live oil and reservoir core, oil saturation has been driven below 5% (Sorc) recovering more than 90% of the waterflood residual oil (Sorw). An ASP treatment, via a single well chemical tracer test, has yielded an Sorc of 1% recovering 96% of the Sorw in the field. However, the process of developing ASP formulations that effectively recover residual oil and represents a commercially viable ASP flood has been challenging. Driving the surfactant retention to low values has required the use of enhanced alkoxylated surfactants and co-solvents that provide low interfacial tension (IFT) micro-emulsions with low viscosity under broad salinity ranges. Kuparuk's rock mineralogy has also played an important role due its heterogeneity and high clay content. Clays with iron bearing minerals and significant ionic exchange have required a systematic study to better understand the effect of reservoir rock on the surfactant treatment. The impact of calcium and magnesium released by the reservoir rock was of primary concern. Significant results from the Kuparuk ASP formulation study are summarized. The performance of formulation components and their overall effect on surfactant retention, as well as rock mineralogy, are analyzed in detail. Experimental methodologies, analytical studies, and adequate core flood practices used to overcome challenges are also discussed. The lessons learned from this study have the potential to be used in other Alaskan fields, unlocking vast and valuable resources from mature reservoirs as well as new discoveries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 teacher head, 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".