Cellulose Nanocrystal Switchable Gel for Improving CO2 Sweep Efficiency in Enhanced Oil Recovery and Gas Storage
Bibliographic record
Abstract
Abstract CO2 injection is regarded as an important method for enhanced oil recovery (EOR) and greenhouse gas control by storing CO2 underground. However, the reservoir heterogeneity and low viscosity of CO2 will result in poor sweep and therefore inadequate oil recovery and inefficient gas storage. The entanglement of biopolymers is a well-known phenomenon that, when controlled, can result in a smart fluid with strong gelation properties. We have shown that when a suitable salt is incorporated into the cellulose nanocrystal (CNC), the fluids undergo gelation upon contact with bulk phase CO2 but remain a flowing liquid otherwise. In this study, we applied this composition-selective trigger to improve the sweep efficiency in CO2 EOR and sequestration. Benchtop tests were performed to observe the gelation time and strength of gel to optimize the chemical concentrations accordingly. Parameters such as CNC and salt concentrations were optimized to tune the gelation time and gel strength. The optimized CNC fluid was tested for its ability to turn to gel within the porous medium as CO2 encounters the fluid. Flow tests were performed in a representative model porous media to analyze the in-situ gelation inferred from pressure profile and fluid production. CO2 and N2 gas were used as the gas phases. A heterogeneous dual-sandpack was used to demonstrate sweep efficiency improvement during CO2 injection. Tuning the chemical concentration enabled us to optimize the gel strength and more importantly gelation time across a wide range, from 5 minutes to more than 48 hours. The gel can be easily broken in contact with nitrogen gas. After CNC+salt fluid was placed in porous media initially containing water and CO2, subsequent injection of CO2 required a very large pressure gradient to initiate flow demonstrating the in-situ generations of gel. Once the flow was initiated, subsequent sequential injections of CO2 and N2 exhibited smaller resistance to N2 than to CO2, consistent with the reversible gel/solution transition in the presence/absence of CO2 observed in batch experiments. Flow tests in heterogeneous dual-sandpack (with permeability contrast of 5) revealed that in-situ gelation diverts injected CO2 almost entirely to the lower permeability layer, which had been almost completely bypassed during injection prior to the CNC+salt injection. The composition-specific trigger, the ability to control the fluid's properties, and the renewable source of nanomaterials will open up an enormous opportunity for CO2 injection processes in heterogeneous reservoirs, in challenging locations (i.e. offshore) and in related applications such as CO2 sequestration.
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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".