Experimental and Numerical Simulation of Combined Enhanced Oil Recovery with In Situ Upgrading in a Naturally Fractured Reservoir
Bibliographic record
Abstract
Abstract The purpose of this research work is to show laboratory experiments conducted at 1500 psi and 350°C, experimentally simulating a reservoir located in the Gulf of Mexico. The experiments conducted used a novel process that involved a hot fluid to be injected with an ultra-dispersed nano catalyst. The results obtained showed that API gravity can be improved permanently as well as its viscosity, with the advantage of no coke or solid deposits formation. Laboratory analyses showed that by using this new process it is possible to enter into the matrix zone, expelling at least partially the oil confined inside. As a consequence of the temperature increase, matrix rock may expand and expel its oil; while temperatures decrease, the pores in the matrix could be contracted, generating additional oil expulsion from this area. As a consequence of this expansion-contraction in the reservoir the reserves could be increased. Also, a change in the permeability appears due to the temperature increases. The present study was focused in oil matrix extraction and in situ oil upgrading from a naturally fractured reservoir of heavy oil as a result of a hot fluid injection with nano catalyst. Moreover, the effects of capillary pressure, mobility, viscous effects, wettability, and gravitational drainage on the process were analyzed. Another aspect that was studied is how the thermal expansion generated as a consequence of the process that could expel the oil confined in the matrix.
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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 | 0.001 | 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".