High Density Solvent Formulation for Applications in the Stimulation of Oil and Gas Wells Using Bio-based Surfactants and Pickering Emulsion Stabilization Techniques
Bibliographic record
Abstract
A common issue in the oil and gas (O&G) industry is reduced fluid flow in the well due to the precipitation of wax or scale. This impairment to flow, also known as skin damage, must be dissolved with chemistries developed for such purposes. Fluid flow is frequently inhibited by hydrocarbon or wax buildup which must be dissolved with a hydrocarbon solvent. Currently, there is no process to place hydrocarbon solvents in the desired location without the use of more solvent, which can be costly. The current approach is to displace with water. Because the solvent is less dense than water, it floats to the top of the column of fluid instead of contacting the damaged area. An emulsion formed by the solvent with salt water will make the hydrocarbon solvent as dense as water. The emulsion can be stabilized using either surfactants or solid particles. The emulsion can be characterized whether it is water-in-oil, or oil-in-water, by using a spread test in which the droplet is spread over an interface of water. A high internal phase water-in-oil emulsion is desired for this application. This means the solvent will be external, and the aqueous phase will be dispersed within, even though the solvent will make up a significantly smaller portion of the solution. In this study, preliminary tests used materials at Secure Energy’s lab in Edmonton, while secondary testing included further testing to identify sustainable and biomaterial-based options. Several successful emulsions were formed using current products available at Secure Energy. A mixture of 15% NaCl brine, WPT-2814 solvent, and SurfSolv AE6 emulsifier is the most promising mixture that will be implemented into the current product line. Testing with cellulose nanocrystals showed promising results for emulsion formulation with solvent and water but not with the saline solution or in combination with other surfactants. Further studies into renewable surfactants or other emulsifying agents may be conducted to add onto the results of this study.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".