Evaluation of Four Thermal Recovery Methods for Bitumen Extraction
Bibliographic record
Abstract
Abstract Around 70% of the world's bitumen resources are deposited in Western Alberta. Surface mining is applicable only to 20% of the total reserves, and in-situ cold production can only be effective to recover the 5% of the initial bitumen in place. This low extraction yield mainly stems from the high viscosities of bitumen, which can be reduced dramatically with the application of thermal recovery. However, economically viable and technically feasible bitumen extraction with thermal methods is challenging. There are also a range of adverse environmental issues associated with thermal methods. As conventional resources decline and since conventional recovery methods are insufficient to extract unconventional reserves effectively, it is necessary to address these issues associated with thermal recovery methods. In this study, we evaluated the recovery characteristics of a Canadian bitumen (8.8 °API-53,000 cp, at 21 °C) with four thermal recovery methods. A two-dimensional physical model was used to evaluate the performances of Steam-Assisted Gravity Drainage (SAGD) and Hot Water Injection (HWI) with SAGD well configuration. Steam Flooding (SF) and In-Situ Combustion (ISC) experiments were performed with two separate one-dimensional experimental set-ups. Experimental conditions were maintained identical at reservoir average conditions. The effluent gas composition, temperature profiles, oil recovery, behavior of fluid (oil, water, steam, and gas) front movements, and the level of oil upgrading were discussed by considering the environmental and economic constrains of the processes. Furthermore, core samples were extracted from the model to evaluate and visualize the sweep efficiency. Experimental findings reveal that ISC yielded the highest (∼90 wt %) and HWI the lowest (∼33 wt %) oil recoveries. In terms of environmental footprints, SF produced the highest amount of gases per barrel oil production, and HWI resulted in the lowest gas production. Among HWI, SAGD, and SF, energy consumption was the greatest in SAGD and the worst sweep efficiency was obtained with SF. Our results recommend a hybrid utilization of thermal recovery methods, specifically HWI with ISC for the extraction of this bitumen. HWI prepares the reservoir for ISC by aiding to increase reservoir temperature and reducing the reservoir heterogeneities with hot water distribution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".