New Composite Viscosity Reducer with Both Asphaltene Dispersion and Emulsifying Capability for Heavy and Ultraheavy Crude Oils
Bibliographic record
Abstract
The recovery of heavy oil from ultradeep wells is a technical challenge faced by the petroleum industry. One of the key issues is the flow assurance of heavy oil inside the wellbore and through the pipelines, i.e., effectively lifting heavy oil from the wellbore and subsequently transporting it smoothly through the pipelines. Traditional chemical methods for reducing the viscosity of heavy petroleum fluids adopt oil-soluble and water-soluble viscosity reducers, but these methods have their limitations. In this work, we develop a new composite viscosity reducer called SDG-2, which combines the advantages of oil-soluble and water-soluble viscosity reducers. The molecular chain of this viscosity reducer is grafted with a high-carbon lipophilic polar group and a salt-tolerant hydrophilic group, providing the reducer with both dispersion and emulsification capabilities. An experimental test shows that, without water, the viscosity reducer SDG-2 can achieve a 50% degree of viscosity reduction (DVR) for the ultraheavy oils tested. A low oil/water interfacial tension (IFT, 0.41 mN·m –1 ) can be achieved when 0.15 wt % SDG-2 is added into the highly saline (2.26 × 10 5 mg·L –1 ) solution. This IFT is significantly lower than the 13.42 mN·m IFT obtained by a 0.60 wt % oil-soluble viscosity reducer, as well as the 8.21 mN·m IFT achieved by a 0.40 wt % water-soluble viscosity reducer. The emulsification capability of SDG-2 is also superior to that of a water-soluble viscosity reducer; oil-in-water emulsion can be obtained when the oil is mixed with 30% (v/v) water and 0.30 wt % SDG-2. Higher DVR values (99%) can be achieved for the Tahe (China), Canada, and Venezuela heavy oils when emulsification occurs. Field tests of the new composite viscosity reducer are conducted in the Tahe oilfield of Xinjiang Province in China. Results show that considerably less light oil is needed to dilute heavy oil to the desired viscosity when the new viscosity reducer is used. On average, a 22.5% increase in heavy oil production rate can be obtained.
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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".