Physical Modeling of Heavy Oil Production Rate in a Vapour Extraction Process
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Bibliographic record
Abstract
Abstract Vapour extraction (VAPEX) process is a promising heavy oil recovery technology because it can cause significant viscosity reduction through sufficient solvent dissolution and possible asphaltene precipitation. Although the VAPEX process has been extensively studied in the past two decades, it is still a challenging technical task to predict its stabilized oil production rate. It has been reported in the literature that the predicted oil production rate can differ from the measured oil production data by several factors to one order. In this paper, physical modeling is conducted to accurately measure the stabilized heavy oil production rate, which is then compared with the theoretical prediction. In the experiment, a rectangular sand-packed VAPEX physical model is used and its porosity and permeability are measured prior to the VAPEX tests. A butane mixture is chosen as a gaseous solvent to extract heavy oil at a constant pressure slightly lower than its dew-point pressure and a constant temperature. The heavy oil VAPEX process is visualized to determine the so-called vapour chamber rising, spreading and falling phases. In particular, the stabilized heavy oil production rate during the vapour chamber spreading phase is measured. Theoretically, the modified Butler- Mokrys analytical model is applied to predict the stabilized heavy oil production rate. It has been found that the modified Butler- Mokrys analytical model can give a good prediction of the stabilized heavy oil production rate in the vapour chamber spreading phase. It is worthwhile to emphasize that the measured permeability of the physical model, the measured solubility and the effective diffusivity of the solvent in the heavy oil should be used in the theoretical prediction. Introduction Effective and economical recovery of heavy oil and bitumen from a large number of heavy oil and bitumen reservoirs in Western Canada becomes a key technical issue because the conventional crude oil production declines rapidly. In 2003, for the first time, the heavy oil and bitumen production exceeded the conventional crude oil production in Alberta1. The high viscosity and low mobility of heavy oil and bitumen cause their primary recovery to be as low as 6~8 percent of the original-oil-in-place (OOIP) 2. As a secondary recovery method, waterflooding may produce some incremental oil. Unfortunately, the overall incremental recovery for waterflooding is rather low due to the quick water breakthrough caused by extremely high mobility ratio. Thermal-based tertiary recovery processes, such as, cyclic steam stimulation (CSS), in-situ combustion (ISC), steam-assisted gravity drainage (SAGD), are currently being applied to enhance heavy oil and bitumen recovery. The maximum oil recovery of a typical CSS process usually does not exceed 20 percent and a subsequent steam flooding process is required to produce the remaining oil in the reservoir3,4. In general, the ISC process is not suitable for recovering highly viscous crude oil (say μ?> 1000 mPa's) 5. The SAGD process6 is rather successful in exploiting the heavy oil and bitumen resources.
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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 it