Evaluation of the Foamability Potential of a Novel Biosurfactant Using the Solution to Advective-Diffusive Transport Model in Porous Media With a Linear Adsorption Trend
Bibliographic record
Abstract
While geological sequestration of anthropogenic carbon dioxide is a technically and economically viable option for reducing emissions to the level required to avoid the predicted 2 degrees Celsius increase of atmospheric temperature by the end of this century, efficient sequestration planning is vital for the achievement of this goal.The petroleum industry has used conventional surfactants in enhance oil recovery projects aimed at prolonging the life span of a field, thereby increasing ultimate reserves. Notable among these is the use of surfactants for injected gas relative mobility control. Therefore, the potential for carbon dioxide mobility control in saline aquifers using surfactant alternating gas injection is huge, given the rich experience that can be tapped from the petroleum industry practice.Considering the expected surfactant loses in surfactant-enhanced geological sequestration similar to that encountered in the petroleum industry, this paper has used the analytical solution to advective diffusive equation that exists in the literature with a linear adsorption model where, adsorption has been used to predict trends in minimum pressure drop required for foam generation. The greatest utility of this work lies in the fact that the analytical solution is related a linear adsorption model related to a novel surfactant found from biological and hydrocarbon sources of geologic origin. This paper, therefore, extends the work of linear adsorption models for this novel surfactant aimed at exploring improved oil recovery potentials; in addition to exploring its potential for efficient geological carbon storage in saline aquifers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".