Enhanced Recovery of Heavy Oil by a Nano-Catalytic In-Situ Upgrading Process
Bibliographic record
Abstract
Abstract Simultaneous in-reservoir upgrading and recovery of heavy oil are experimentally studied by using a continuous setup filled with carbonate cores. Upgrading reaction products as well as the recovered oil are analyzed in order to investigate the recovery mechanisms associated with this process. In-situ upgrading technology (ISUT) is based on injection of high molecular weight (low quality) cut of oil, e.g., vacuum residue (VR), together with ultradispersed nano-catalyst and hydrogen. By injecting VR, catalyst, and hydrogen, the catalytic nano-particles deposit in the rock around an injection well, where the upgrading reactions occur. In this study, first, upgrading reactions happen inside a core packed container at the temperature, pressure, and residence time of 360 °C, 10 MPa, and 36 h, respectively. Subsequently, the hot reaction products are directed into another cylinder filled with carbonate cores to displace the heavy oil in place. There are two main steps in a reservoir during ISUT. First, the injected VR is converted to lighter products during hydroprocessing reactions. Then the upgraded liquid and gaseous products along with hydrogen will displace the heavy oil toward production wells. At the conditions of this experiment in the reactor, 38 wt% of the VR cut is converted to lighter products with 1 wt% gases (mainly H2S and hydrocarbons with 1 to 5 carbon atoms) and 4.8 wt% naphtha cut (hydrocarbons with 5 to 12 carbon atoms). These light products act as solvents in the areas farther from the reaction zone and enhance the recovery of heavy oil. In addition, high temperatures, mechanical push, and rock matrix thermal expansion improve the oil displacement in the carbonate rock. By enhancing the oil recovery and permanently upgrading heavy oil in one single stage, the need for diluent addition and steam generation is minimized, which makes ISUT economical and environmentally favorable. In an innovative experimental setup, both upgrading and recovery steps in the ISUT process are carefully analyzed.
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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) | 0.000 | 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) | 0.000 | 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".