Microemulsion Formulations with Tunable Displacement Mechanisms for Heavy Oil Reservoirs
Bibliographic record
Abstract
Summary Waterflooding of heavy oil reservoirs is commonly used to enhance their productivity. However, preferential pathways are quickly developed in the reservoir because of the significant difference in viscosity between water and heavy oil and, hence, the oil is trapped. Here, we propose a platform for designing ultralow interfacial tension (IFT) solutions for reducing the capillary pressure and mobilizing the heavy oil. In this study, we formulated mixtures of organic acids and bases. We tested three different formulations: an ionic liquid (IL) formulation in which the bulk acid [4-dodecylbenzene sulfonic acid (DBSA)] and base [tetra-N-butylammonium hydroxide (N4444OH)] were mixed using general protocols for IL synthesis; an acid/base solution (ABS) in which the acid (DBSA) and base (N4444OH) were mixed in low weight fractions directly in water; and an acid salt/base solution (ASBS) in which the acid salt [sodium dodecylbenzene sulfonate (SDBS)] was used instead of the acid. All the formulations have a 1:1 stoichiometric ratio of acid and base. Salinity scans were conducted to determine the optimum salinity that gives the lowest IFT for each formulation. Corefloods were conducted in hydrophilic and hydrophobic sandpacks to evaluate the three formulations at their optimum salinities for post-waterflood heavy oil recovery. The IL and ABS formulation are acidic solutions with a pH of approximately 3. The ASBS formulation is highly basic with a pH of approximately 12. None of the formulations salted out below 14 wt% of sodium chloride (NaCl), whereas the conventional surfactant, SDBS, precipitated at a salt concentration of less than 2 wt% of NaCl. The formulation solutions (1 wt%) have different optimum salinities: 2.5 wt% NaCl for ASBS and 3 wt% NaCl for IL and ABS. Although the IL and ABS have the same composition and molar ratio of the components, their performances are completely different, indicating different intermolecular interactions in both formulations. Corefloods were conducted using sandpack saturated with Luseland heavy oil (∼15,000 cp) and a fixed Darcy velocity of 12 ft/D. A slug of 1 pore volume (PV) of each formulation was injected after waterflooding for 5 PV followed by 5 PV post-waterflooding. In the hydrophilic sandpacks, IL and ABS formulation produced an oil bank consisting mainly of a water-in-oil (W/O) emulsion, with oil recovery that was 1.7 times what was recovered by 11 PV of waterflooding solely. The majority of the oil was recovered in the 2 PV of waterflood after the IL slug. ASBS formulations produced oil-in-water (O/W) emulsions with prolonged recovery over 5 PV waterflooding after the ASBS slug. The recovery factor for ASBS was 1.6 times that recovered for 11 PV of waterflooding only. In the hydrophobic sandpacks, the ASBS formulation slightly increased the recovery factor compared with only waterflooding, whereas for IL and ABS formulations, the recovery factor decreased. In this work, we present a novel platform for tuning the recovery factor and the timescale of the recovery of heavy oil with a variable emulsion type from O/W to W/O depending on the intermolecular interactions in the system. The results demonstrate that the designed low IFT solutions can effectively reduce the capillary force and are attractive for field applications.
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 | 0.000 | 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 | 0.001 |
| 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 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".