Experimental Study of Nanocatalytic In-Situ Upgrading for Heavy Oil Production From Naturally-Fractured-Carbonate Reservoirs
Bibliographic record
Abstract
Abstract Nanocatalytic in-situ upgrading is a novel oil recovery method that involves chemical, thermal and miscible processes. In this work the main oil recovery mechanisms of nanocatalytic in-situ upgrading were studied, particularly the ones that promote additional oil production from low matrix permeability blocks. Heavy oil recovery from Silurian dolomite cores was studied using a cylindrical core holder set-up. Fractures in the system were represented by a gap between the core sample and core holder wall. Oil recovery experiments were conducted in batch-mode using hydrogen and a trimetallic nano-catalyst. The cores were fully saturated with heavy-oil and the fractures were filled with hydrogen and vacuum residue with ultra-dispersed nano-catalyst at 300 °C and 1000 psig. The produced oil from the matrix was collected and the recovery factor for each experiment was calculated. Moreover, the residual oil in the core was extracted using a solvent. Both samples (i.e., produced and residual oil) were characterised by laboratory measurements and analytical techniques in order to assess oil quality distribution. Experimental results revealed a significant increment in oil recovery with hydrogen injection. This increment suggests that during nanocatalytic in-situ upgrading oil is produced due to the presence of hydrogen in gas form. Results also demonstrated that, by use of an ultra-dispersed Ni-W-Mo nano-catalyst, the oils contained in both the fracture and matrix, were upgraded. This research fosters the understanding of the main recovery mechanisms from carbonate matrix blocks by use of nanocatalytic in-situ upgrading. This study contributes to better understanding a recovery technique that will unlock heavy-oil resources contained in carbonate rocks.
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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".