A Microbial Exopolysaccharide Produced by <i>Sphingomonas</i> Species for Enhanced Heavy Oil Recovery at High Temperature and High Salinity
Bibliographic record
Abstract
Microbial exopolysaccharides secreted by microorganisms during metabolic processes have been widely used in biotechnology because of their environmentally friendly and renewable nature. This study evaluates the potential of a novel microbial exopolysaccharide, diutan gum, which is produced by Sphingomonas species, for enhanced heavy oil recovery at high temperature and high salinity. In addition, two conventional polymers [xanthan gum and partially hydrolyzed polyacrylamide (HPAM)] used in oil exploitation are compared under the same conditions. It is found that the steady apparent viscosity and dynamic modulus of aqueous diutan gum solutions are not sensitive to the temperature and virtually independent of the salinity, while those of xanthan gum and HPAM significantly decrease at high temperature and high salinity. The retention values of the apparent viscosity and the dynamic modulus of diutan gum at 90 °C and 244 121 mg·L –1 salinity are greater than 90%. The gel-like structure of diutan gum is dependent on the shear rate rather than the shear time and the aging time. The thermal stability and salt tolerance of diutan gum are mainly attributed to the stability of the gel-like molecular structure, which is greatly related to the double helix. Flow tests in sandpacks demonstrate the excellent mobility control capacity of diutan gum in porous media, and the permeability reduction of porous media is attributed to the adsorption and interception of diutan gum at high temperature and high salinity. Sandpack flooding experiments confirm that the heavy oil recovery efficiency of diutan gum is raised by 20.9% OOIP and is higher than that of either xanthan gum (9.3%) or HPAM (5.4%) at 90 °C and 244 121 mg·L –1 salinity. It is believed that diutan gum will be a promising oil recovery agent for enhanced oil recovery in high-temperature and high-salinity reservoirs.
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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) | 0.001 | 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 | 0.000 |
| 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".