Effect of Drainage Height and Grain Size on the Convective Dispersion in the Vapex Process: Experimental Study
Bibliographic record
Abstract
Abstract Interest in the vapor extraction (Vapex) process for heavy oil and bitumen recovery has considerably grown as a viable and environmentally friendly alternative to the currently used thermal methods. The potential for success of Vapex process is even more attractive in some scenarios that preclude the thermal methods. Presence of overlying gas cap and/or bottom water aquifer, thin pay zones, low thermal conductivity, high water saturation, unacceptable heat losses to overburden and underburden formations etc., are some of the limitations with the thermal techniques, which can be potentially overcome by vapor extraction implementation. However, predicted low production rates by previous researchers for field application of Vapex technique remain a serious barrier to commercial applications of the process. The scale-up methods that have been used by previous workers for translating the laboratory results to field predictions were primarily based on the reservoir transmissibility. An analytical model developed by Butler and Mokrys1 showed that the oil rate should be proportional to the square root of reservoir transmissibility. The effect of convective dispersion between solvent and virgin heavy oil in porous media were ignored in developing this model. The main objective of this work is to develop an improved scale-up method for the Vapex process using physical model experiments carried out in models of different sizes. In this paper we report the results of a new series of experiments that extend the previously reported results of Karmaker and Maini2 to a significantly wider range of model heights. These new experiments employed a new design of slice type physical models that places the sand-pack in the annulus formed by two cylindrical pipes. Combining the new results with the previous data of Karmaker and Maini2, we show that the transmissibility based scaling up method seriously under-predicts the results at larger scales. This observation suggests that much higher rates can be expected in the field implementation of the Vapex process. A new correlation has also been proposed for scaling up the experimental data to the real field cases. It indicates the height dependency of the convective dispersion contribution, which can be the dominant mass-transfer mechanism for the process, to be higher order than previously postulated. Experimental results from this work show that the stabilized rate is a function of drainage height to the power of 1.1 to 1.3, instead of the square root functionality of the Butler and Mokrys2 model.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".