Experimental Study of Nanocatalytic In-Situ Upgrading for Heavy Oil Production From Naturally-Fractured-Carbonate Reservoirs
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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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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 | 0.001 |
| 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 it