Synergy Between Commodity Molecules and Nanoparticles as Steam Mobility Control Additives for Thermal Oil Recovery
Bibliographic record
Abstract
Abstract Achieving mobility control of steam in porous media has been approached by the implementation of foaming surfactant additives; however, foam generated with surfactants lack stability at high temperatures (≥ 200 °C). In this study, a nanofluid was formulated for steam co-injection to address common issues with surfactants as steam additives. The nanofluids were formulated using synergistic interaction of nanoparticles and surfactants (both readily available) to address thermal stability, and foam stability at the conditions encountered in thermal enhanced oil recovery processes such as steam assisted gravity drainage. Zeta potential analysis, static tests, and high temperature aging tests were performed to obtain the ideal mixture of nanoparticles and surfactants. A steam/foam core flooding apparatus was used to evaluate the mobility reduction of nanofluids using multiple differential pressure transmitters situated along the length of a 40 cmsand pack to confirm foam propagation. The tests were first conducted at 200 °C with nitrogen as a non-condensable gas carrier. Upon confirming nanofluid mobility reduction characteristics at 200 °C with gas, a successful nanofluid was co-injected with superheated steam at backpressure corresponding to a saturation temperature of 200 °C inside an oven with that set point. Mobility reduction characteristics were obtained for each formulation by normalizing the differential pressure of additive multiphase flow to their respective baselines. Statictests and high-temperature core floods demonstrated a strong synergy between appropriate combinations of surfactants and nanoparticles.None of thesurfactants and nanoparticlesyielded mobility reduction when used on their own; however, when selected according to a design basis, nanoparticle/surfactant mixtures exhibit strong mobility control with steam as well as hot gas. The foam produced in the porous media was held in a visual cellat 150 °C for 24 h with no loss in foam height. Furthermore, foam generated with the nanoparticle-surfactant hybrid remained stable in static tests in the presence of heavy crude oil for more than one week. The primary novelty of this study is the ability to use less exotic surfactants and nanoparticles as steam mobility control agents, increased foam stability in the presence of oil, and demonstrable synergy between commodity molecules (i.e., surfactant) and nanoparticles at steam flooding conditions. The successful additive mixture was developed with scalability in mind such that manufacturing will not hinder the feasibility of scale-up for industrial use.
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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 | 0.000 |
| 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".