Effect of Mixed Gas Solvent Injection on Performance of the Vapex process in an Iranian Heavy Oil Sample
Bibliographic record
Abstract
Abstract Vapor Extraction (Vapex) process is a viable and environmentally friendly alternative to currently used thermal methods. It involves injection of vaporized hydrocarbon solvents into the reservoir to decrease the heavy oil viscosity by dilution and de-asphalting. In high pressure reservoirs solvent should be accompanied by a non-condensable gas to prevent condensation by lowering the dew point of the gas mixture. This experimental work investigates the effects of mixed gas solvent injection on performance of the Vapex process in an Iranian heavy oil sample (Kuh-e-Mond reservoir) with very high viscosity and asphaltene content. The experiments were performed at low, medium and high pressure (110, 200 and 400 psig) on a 2-D visual model. Propane was injected as solvent in all of the experiments whereas methane and carbon-dioxide were applied as carrier gases. In general injecting mixed gases at high pressure caused some changes in behavior of the system which were not observed in pure solvent injection. The production rate trend showed more fluctuations; solvent chamber grew more in lateral direction rather than in depth, and less asphaltene precipitation was observed in the system. These can affect the selection of vertical spacing between well pairs and also the process application in lower permeability conditions as we may not face plugging of pores or production well due to asphaltene precipitation at lower permeabilities. In addition, the effects of solvent concentration and carrier gas type on production parameters and produced oil properties were studied. These parameters include cumulative production, production rate, recovery factor and residual oil saturation as well as density, viscosity and asphaltene content of the produced oil samples. Introduction Most of the world's oil reserves are heavy and viscous hydrocarbons which are difficult to produce. Heavy oil, extra heavy oil and bitumen make up about 70% of the world's total oil resources1. Today with global increasing demand for more oil, decline in production from conventional reservoirs and consequent increase in oil prices, heavy oil seems to be a promising resource in the future of petroleum industry. In Iran, the proved and probable heavy oil reservoirs are mostly located in southwestern part of the country. The abundance of these heavy oil deposits suggests systematic investigation of EOR techniques for future exploitation2. Very low primary recovery factors reveal the importance of applying EOR techniques to heavy oil reservoirs. Simple methods such as water flooding can not enhance the production to a desirable extend as the mobility ratio will cause early breakthrough without being able to sweep a considerable portion of the reservoir. So the main goal in designing EOR techniques for heavy oil reservoirs should be reducing the viscosity as much as possible which can be done by introducing an external agent. There are two main mechanisms in which the external agent can act to lower the viscosity, heat transfer and mass transfer. This external agent can be air, steam or solvent as in case of in-situ combustion, steam flooding/Cyclic Steam Stimulation (CSS)/SAGD and the Vapex process respectively.
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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 | 0.001 | 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".