Lab Study of High WAT Wax Deposition Reduction with Wax Inhibitors and Dispersants
Bibliographic record
Abstract
Abstract Canadian crude oil and pigged wax from the Montney formation show high wax appearance temperatures (WAT) and experience severe deposition issues during production and transportation. Several commercial wax inhibitors and wax dispersants were studied in the crude oil and reconstituted oils (pigged wax added back to the crude oil and dodecane model system), to minimize the wax deposition by a systematic lab screening protocol. Suitable wax inhibitors (WI) and dispersants were selected and formulated at optimized dosage to efficiently reduce the wax deposition at close to field condition. The crude oil and reconstituted oils were utilized to study the high WAT wax performance with different types of wax inhibitors and dispersants. This included ethylene vinyl acetate (EVA), alkylphenol formaldehyde resin (AFR), acrylic copolymer (AC), α-olefin maleic anhydride copolymer (AOMAC) and several surfactant-based wax dispersants (WDs). A pour point tester was employed as the initial screening tool to determine the pour point and detected wax appearance temperature (DWAT). Multiple Light Scattering (MLS) was used to evaluate the dispersions of wax in the oil. Dynamic wax deposition tests by capillary flow through (CFT) and dynamic flow loop (DFL) systems were used to verify the wax deposition reduction efficiency, and to study the effect of the test parameters on wax deposition. The reconstituted oils had higher WAT (>55 °C) than produced oil. The screening tests showed that EVA significantly reduced the DWAT and pour point of the crude oil but was not very efficient in the reconstituted oil. Both AFR and AC reduced the DWAT and pour point but were not as efficient as AOMAC. AOMAC provided the lowest DWAT in the reconstituted oil. It was interesting to find that surfactant-based dispersants also reduced the DWAT of the reconstituted model oil. The top performing WIs and dispersants were then tested by CFT wax deposition system at a flowrate of 1.5 cm3/hr. For the crude oil at 10 °C, 225 ppm AOMAC WI was needed to efficiently reduce the wax deposition in the CFT system. A lower dosage was required in the DFL system. It was also found that wax inhibitor and dispersant together further reduced the reconstituted model oil wax deposition in the CFT system. MLS and bottle tests showed that the WDs helped to disperse the wax in both oil and aqueous phases. From this systematic WI study on kinetic and dynamic behaviors of high WAT wax deposition, a synergy was observed between wax inhibitors and dispersants. Further investigation is needed to understand how they work together. The specially designed laboratory screening protocol helped to understand the structure and performance relation, efficiently formulate the WIs/dispersants, and optimize the treatment dosages. The inclusion of surfactants/dispersants with WIs could further mitigate wax deposition and be a more cost-effective approach.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".