Enhanced Oil Recovery from Austin Chalk Carbonate Reservoirs Using Faujasite-Based Nanoparticles Combined with Low-Salinity Water Flooding
Bibliographic record
Abstract
Recently, the application of nanoparticles for enhancing oil recovery (EOR) in carbonate reservoirs has received great attention from various researchers across the oil and gas industry. In contrast to sandstone reservoirs, carbonates are naturally neutral wet or preferentially oil wet and, therefore, the recovery of oil from these reservoirs by waterflooding techniques is relatively low and inefficient. Hence, the addition of chemical agents can modify rock wettability and increase the efficiency of the waterflooding process. The role of nanoparticles and their implementations in the field of oil recovery has been highlighted by many researchers in the past, due to their attractive features and characteristics. However, choosing the appropriate nanoparticles is not the only limiting factor to guarantee better performance in EOR but also depends on their stability and dispersion under aqueous conditions. Accordingly, many metal oxides or silicate-based nanomaterials have been subjected to surface modifications, following some complex and costly ineffective functionalization steps before their application. In this study, novel and stable nanomaterials of faujasite were synthesized at mild conditions without following any surface modification steps to alter the wettability of Austin Chalk carbonate rocks from oil wet to strongly water wet in the presence of low-salinity water (LSW). The synthesized nanoparticles were well characterized by scanning electron microscopy (SEM), transfer electron microscopy (TEM), X-ray diffraction (XRD), dynamic light scattering (DLS), and ζ potential to confirm their surface identity, functionality, morphology, and stability. The prepared nanofluids from the synthesized nanoparticles were tested in comparison to brine for their EOR efficiency in carbonate cores. The EOR performance was investigated by interfacial tension (IFT), contact angle, spontaneous imbibition, and displacement tests. The results showed that, compared to formation brine and LSW, the formulated nanofluid could notably alter the rock wettability from strong oil wet to strong water wet. To confirm this, a core-flooding test was performed, which further reiterated the capability of these nanofluids as effective EOR agents in hydrocarbon carbonate reservoirs by recovering an additional 9.6% of OOIP. Consequently, on the basis of the obtained findings, these faujasite-based nanofluids provide a prospect of being applied in EOR in carbonate formations.
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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 | 0.000 |
| 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 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".