Enhancing Well Productivity by Adding Nanofluid to Fracturing Water: A Cardium Case Study
Bibliographic record
Abstract
Abstract Recent field data obtained from 2,388 oil and gas wells in the Cardium Formation indicate the outperformance of Nano surfactant (Nanofluid) in improving well productivity and load recovery, which supports its potential for further field-scale implementations. Therefore, this study presents a standard laboratory protocol to evaluate the impact of adding a NF (Nanofluid) to fracturing water on improving oil recovery from high- and low-permeability (tight) rock samples. We conduct systematic laboratory experiments in two stages using the in-situ rock and fluid samples obtained from different wells in the Cardium Formation. In stage 1, we evaluate the wettability behavior of the reservoir rock samples by conducting comparative oil/brine co-current spontaneous imbibition experiments and equilibrium contact-angle measurements. In this stage, we conduct Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray Spectroscopy (EDS) experiments on the end-pieces of the plugs for pore-scale visualization and elemental composition analysis, respectively, to support our observations from the wettability experiments. In stage 2, we conduct counter-current imbibition experiments on oil-saturated plugs to evaluate the impact of NF on enhancing oil recovery from rock samples with different permeabilities. The results show that oil imbibes faster and more than brine into the dry twin plugs such that the imbibed oil volume is 10% of the bulk volume (BV) in 170 hours, while the imbibed water volume is only 3% of the BV in 330 hours. The contact-angle results indicate that in the presence of air, the rock samples have more affinity toward oil than brine, which is consistent with the imbibition results. The counter-current imbibition (soaking) experiments show that adding NF to water does not improve oil recovery from high-permeability plugs such that oil recovery factor in the presence and absence of the NF sample is relatively the same and is almost 31% in 36 days. However, adding NF to water significantly improves oil recovery from the oil-wet low-permeability samples such that oil recovery in the absence of NF is 12%, while that in the presence of NF is 31% in 58 days. Adding NF to tap water (TW) significantly reduces its IFT (interfacial tension) with oil from 10.29 mN/m to 0.59 mN/m (more than 17 times IFT reduction), which can explain the ability of NF to extract oil from small pores in tight rocks and considerable improvement in the ultimate oil recovery. Additionally, NF accelerates oil recovery from the tight-rock samples such that TW recovers 12% in 58 days, while NF recovers the same amount of oil in 13 days.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".