Reducing the Permeability of Sandstone Porous Media to Water and CO2: Application of Bovine Carbonic Anhydrase Enzyme
Bibliographic record
Abstract
Abstract This paper presents the results of an investigation on the application of the enzyme bovine carbonic anhydrase for reducing the permeability of a sandstone porous medium during CO2 flooding. This process provides a method for improving conformance of injected water and CO2 in an absolutely environmentally friendly manner. Carbonic anhydrase enzyme accelerates the hydration reaction of CO2 with water. Hence, in presence of carbon dioxide and divalent ions, such as calcium, this enzyme leads to rapid precipitation of calcium carbonate. The precipitation reaction causes reduction in the permeability of the flooded regions of the reservoir. Therefore, upon subsequent injection of CO2 and water, injected fluids would flow through the unswept parts of reservoir, improving the conformance of the injected CO2 and water. Experiments were carried out to investigate the effect of various enzyme concentrations, temperature and pH on the precipitation reaction. Enzyme concentrations at 2, 4 and 8 micro mole per litter were tested. Also, effect of temperature was studied by performing experiments at room temperature, 30 °C and 55 °C. A mathematical model was developed to predict the extent of precipitation of calcium carbonate. Subsequently, coreflood experiments, using Berea sandstone cores, were conducted and the degree of reduction in the permeability of the cores was measured. Also, in order to investigate the effect of injection scheme on permeability reduction, two different methods of injection were tested. For all sets of flow experiments, the permeability of the core was reduced to less than half of its initial value.
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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) | 0.000 | 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 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".