Impact of Divalent Ions on Heavy Oil Recovery by <i>in situ</i> Emulsification
Bibliographic record
Abstract
Abstract Many reservoir formation brines are characterized by high salinity and contain high concentrations of divalent ions such as calcium, magnesium, and potassium. These challenging conditions can render the surfactants ineffective during chemical flooding for enhanced heavy oil recovery. Various brine types can have an impact on the stability of emulsions generated with chemicals as chemicals have various resistant levels toward hard divalent ions and salinities. To investigate the impact of brine hardness on heavy oil‐in‐water emulsion stability, glass tube experiments, microscopic visualization and sandpack flooding experiments, and Hele‐Shaw visualization experiments were conducted in this study under low‐salinity/hard‐brine, high‐salinity/hard‐brine conditions using commercial chemicals, which are designed for specific reservoir brine conditions. Recovery results demonstrated that complex colloidal solution introduced in the previous study with silica and Dodecyltrimethylammonium bromide (DTAB) along with screened chemicals from glass tube tests in this study can enhance heavy oil recovery significantly with an addition of low concentration of xanthan gum (Lee and Babadagli 2018). The results confirmed the robustness of the complex colloidal solution formula to enhance oil recovery with low concentration of polymer under any reservoir brine conditions. The study also demonstrates the potential of polymer as an emulsion stabilization additive for enhanced heavy oil recovery by in situ emulsion generation. Polymer effects seemed to be particularly dominant under the low‐salinity conditions than high‐salinity conditions.
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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".