Characterising functionalised nanoparticles for improving fluid flow for CCUS in legacy hydrocarbon reservoirs 
Bibliographic record
Abstract
In recent years there has been rapid development of nanoparticles (NPs). Nanoparticles can be used both as a probe into restricted spaces, such as the pores within a reservoir rock, and as tools for altering wettability or deliberately blocking pore throats to enhance fluid movement in less connected pores. Silica nanoparticles can have functional surfaces allowing them to react specifically to oils or water. Nanoparticles can be used to enhance oil production by releasing oil on mineral surfaces and improving fluid flow. However, they also have the potential for improving CO2 flow in CCUS reservoirs while enhancing the pore volume available for CO2 storage. In this paper we evaluate the performance of different non-functionalised and functionalised nanoparticles for enhancement of oil production, CO2 emplacement and gas flow. Different forms of silica NPs have been made, either unfunctionalized, or functionalised with branched amino-based polymer (hydrophilic) or a silane-based agent (hydrophobic). Their stability has been characterised using a range of laboratory methods. The microscopic performance of the nanoparticles has been measured using contact angle measurements. Their ability to enhance oil production and CO2 emplacement has been tested using imbibition and drainage experiments. The contact angles, measured in the presence of brine, no modified silica NPs, branched amino-based polymer (hydrophilic) modified silica NPs and silane-based agent (hydrophobic) modified silica NPs showed contact angle values of approximately 110°, 116°, 124°, and 136°, respectively. These results show that introduction of nanofluids led to a change in substrate wettability from water-wet to strongly water-wet. Notably amongst the tested nanoparticles the Silane-based NPs demonstrated the highest hydrophilic surface. The spontaneous imbibition tests conducted on various sandstone cores revealed that silane-based NPs yielded the highest oil recovery rates among the tested NPs. Specifically, these nanoparticles showed an approximate 12% and 50% enhancement in oil recovery compared to non-modified silica nanoparticle, and branched amino-based polymer (hydrophilic) modified silica NPs. In summary, nanofluids have been shown to substantially improve the wettability alteration of the rock surface from oil-wet to water-wet, which can lead to improve the volume and flow characteristics of legacy CCUS prospects. Our future plan is to investigate the enhancement of carbon dioxide (CO2) solubility in brine through the utilization of the prepared nanoparticles, with the objective of advancing carbon capture technologies.
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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".