Heavy Oil Catalytic In Situ Upgrading in Fractured Carbonate Reservoirs. Fluid Distributions and Occurring Reactions Monitored with Marker Hydrocarbons
Bibliographic record
Abstract
The effectiveness of a novel catalytic heavy oil in situ upgrading technology (ISUT) in fractured carbonate reservoirs with varying rock properties was investigated in the present study carried out with Mexican heavy crudes. The tests conducted in batch (300 °C under 1000 psig H 2 for 48 and 96 h) and continuous operation modes (360 °C, 36 h residence time, and 1450 psig pressure) were utilized to achieve up to ∼30 wt % vacuum residue (VR) conversion and up to 65 wt % recovery. By dispersing hydroprocessing catalysts, such as Ni–Mo and/or Ni–Mo–W, in VR media and in the presence of H 2, efficient upgrading was achieved with stable produced oils. Distributions of molecular markers introduced into the matrix oil or into the injected VR (1-Me-naphthalene, phenanthrene, α-cholestane, fluorenone) were monitored via gas chromatography (GC)-simulated distillation and/or GC–mass spectroscopy, indicating movement of hydrocarbons toward and out from the porous space of the carbonate matrices, suggesting successful penetration of the upgraded VR into the rock’s porous space. Detection of hydrogenated compounds derived from spiked markers provided evidence of hydrotreating reactions occurring during ISUT processing. The study proposes a possible oil production mechanism that combines rock thermal expansion, solution gas drive (H 2 ), and solvency from generated upgraded light ends, explaining oil production within carbonate matrices when ISUT is applied to naturally fractured reservoirs. These findings highlight the immense potential of ISUT for unlocking vast oil reserves present in carbonate reservoirs worldwide.
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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) | 0.000 | 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 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".