Performance of Drainage Experiments With Orinoco Belt Heavy Oil in a Long Laboratory Core in Simulated Reservoir Conditions
Bibliographic record
Abstract
Summary When some heavy-oil reservoirs are produced using gas drive, they show three important features: low production gas/oil ratios, higher-than-expected production rates, and relatively high oil recovery. The mechanism for this unusual behavior remains controversial and poorly understood, though the term "foamy oil" is often used to describe such behavior. The impetus for this work stems from some recent projects performed in the Orinoco belt, Venezuela. There exist nearly one trillion bbl of heavy oil (oil in place) in this region on the basis of a recent evaluation. Two crucial issues must be addressed before or during designing production projects: What is a suitable method for evaluating the foamy-oil drive mechanism that plays a major role during such oil recovery, and how do we obtain a reasonable percentage of ultimate oil recovery? Unfortunately, it is still difficult to give good explanations for these two issues, although several studies were performed. This paper attempts to present better explanations for these two issues using experimental drainage in a long laboratory core in simulated reservoir conditions. Our experiments show that ultimate oil recovery for the heavy oil in the Orinoco belt can be as high as 15-20%. This high recovery comes from three contributions: fluid and rock expansion, foamy-oil drive, and conventional-solution-gas drive. Approximately 3-5% of recovery is from fluid and rock expansion, 11-16% from foamy-oil drive, and 2-4% from conventional-solution-gas drive. This ultimate-oil-recovery percentage is much higher than the 12% that has been used in the field-development plan for the Orimulsion project. The experiments performed and their findings obtained in this paper are representative at least in the Orinoco belt region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".