Alkaline-Free Surfactant Polymer Process for Heavy Oil
Bibliographic record
Abstract
Abstract Chemical EOR methods have become an increasingly attractive option for heavy oil reservoirs where thermal methods (such as SAGD) cannot be applied, like in thin reservoirs. Polymer flooding in heavy oil recently proved to be a viable recovery method. The use of surfactants for heavy oil is reported only in a limited number of cases and mostly in combination with alkali to benefit from the generation of in-situ surfactants. However, operational issues (such as scale or corrosion) associated to the use of alkali as well as negative impacts on project logistics are often reported. Objective of this work is to demonstrate at lab scale the efficiency of alkali-free surfactant/polymer process in the context of heavy oil reservoirs. The present investigation was focused on a Canadian heavy-oil (14°API and 1400 cP) in representative reservoir conditions (high permeability sandstone, temperature of 35°C, low salinity). A dedicated synthetic surfactant formulation was designed using a screening methodology based on a robotic platform. Ultra-low interfacial tensions were evidenced from phase behavior and confirmed by spinning-drop tensiometry. Oil recovery performances of the surfactant formulation were then evaluated in corefloods. Cores at Swi were first polymer flooded until no oil is produced to reach a pseudo-residual oil saturation. A surfactant-polymer formulation was then injected after the polymer flood. Results show that additional oil was produced as a continuous oil bank, corresponding to 90% ROIP. This indicates that the surfactant was able to mobilize most of the residual oil. The results of this exploratory investigation show that alkaline-free surfactant-polymer processes could be applied to heavy oil reservoirs while minimizing operational issues. Complementary work are also be presented on optimization of the process including injection strategy improvement and surfactant dosage reduction.
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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 | 0.000 |
| 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 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".