Cyclic Steam Injection With Solvents as Method of Thermal Recovery for Heavy and Extraheavy Oils: Laboratory Tests
Bibliographic record
Abstract
Abstract Venezuela has the biggest reserves of heavy and extra heavy oils in the world. High viscosity of heavy and extra heavy oils is the main difficulty for its exploitation and production. Steam injection is a possible enhanced oil recovery (EOR) technique most widely applied to this type of oils based on temporary viscosity decrease. Commonly, it is used in Venezuela and Canada; however, factors as steam availability at field operations and low values of displacement efficiency achieved along the process by oil viscous forces have affected its possible use in the future. Nowadays, PDVSA Intevep is evaluating the potential application of the method using some light oil cuts as solvents or additives which are from refineries located nearly to the fields under exploitation operations. So far, static tests have been carried out by using methane, heavy oil from a Venezuelan field (9°API y 41500cP@43 °C) and solvents as light oil cuts (naphtha, kerosene) and two types of effluents from some refinery processes which will be named cut A and B along this investigation at saturated steam conditions. Displacement tests using displacement cells at reservoir conditions in porous media (253 °C, 400 psi, 30% porosity value and 4 Darcies, permeability) have also been performed in order to determine percentages of oil recovery. Results indicated no net differences between the solvents selected during oil - solvent compatibility tests. Nevertheless, the effluents named 1 and 2 increased percentages of recovery factor notably along displacement tests obtaining values around 50 % in comparison to conventional displacement tests by using steam only. Hence, the use of this type of effluents which are cheaper than the light oil cuts selected is being recommended as potential application at field operations in Venezuela taking into account further studies as well as further technical and economical evaluations.
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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".