Physical and Numerical Simulations of Subsurface Upgrading using Solvent Deasphalting in a Heavy Crude Oil Reservoir
Bibliographic record
Abstract
Abstract Physical and numerical simulations of subsurface upgrading using solvent deasphalting (SSU-SDA) at laboratory conditions will be presented using a heavy crude oil and propane as solvent. In this work, propane flood experiments were carried out in a live crude oil (8.8°API) saturated sand at 120°F and 1000 psi. The results showed oil recovery of 85 wt.% with increases of API up to 14°API for the produced crude oil. Using lab characterization data, a new asphaltene precipitation model was developed that involves four pseudo components to numerically simulate the lab experiments. The pseudo components used are Deasphalted Oil, Heavy Fraction, and Soluble and Solid Asphaltenes. History match showed very good agreement between the experimental and calculated oil and gas rates and cumulative oil. Also, reasonably good match between lab and theoretical API of the produced oils was found throughout the propane flood experiments. Using this model, a field-scale one-well pair in SAGD configuration was simulated for steam only and two steam+ propane cases (10:1 and 1:1 vol. ratio) in a typical heavy crude oil reservoir. Results showed accelerated oil production and higher API crude in the presence of propane in comparison with the steam only case. For the 1:1 steam/propane case, the model predicted that the oil quality improved enough to make the oil transportable through a pipeline. Subsurface upgrading via solvent deasphalting is an innovative concept that has the potential of being a game-changer technology in terms of acceleration of oil production, lower CAPEX and OPEX and environmental benefits. The results presented show the potentiality of SSU-SDA for the exploitation of the vast reserves of heavy and extra heavy oils available in the world.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".