New Insights on Chemical EOR Processes for Heavy Oil
Bibliographic record
Abstract
Abstract Chemical EOR methods have become an increasingly attractive option for heavy oil reservoirs where thermal methods cannot be applied, like in thin reservoirs. The use of surfactants for heavy oil is only reported, both at lab and field scale, 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 with the use of alkali as well as negative impacts on project logistics are often mentioned. The objective of this work is to demonstrate at lab scale the efficiency of alkaline-free surfactant-polymer processes in the context of heavy oil reservoirs. The present investigation is 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 is designed using a screening methodology based on a robotic platform. Ultra-low interfacial tensions are evidenced from phase behavior and confirmed by spinning-drop tensiometry. Oil recovery performances of the surfactant formulation are then evaluated in corefloods. Cores at Swi are first polymer flooded until no oil is produced to reach a residual oil saturation. Surfactant-Polymer formulations are then injected. Typical results show that additional oil is produced as a continuous oil bank (up to 100% ROIP depending on the slug size) and with a moderate adsorption if a salinity gradient strategy is applied (typically 0.2 mg surfactant per g of rock). This indicates that the surfactant is 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 will also be presented on optimization of the process through injection strategy improvement and surfactant dosage reduction as well as on extrapolation of the lab results to field-scale technical and economical feasibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".