Design and Optimization of Hybrid Ex Situ / In Situ Steam Generation Recovery Processes for Heavy Oil and Bitumen
Bibliographic record
Abstract
Abstract Given the enormous capital costs, operating expenses, flue gas emissions, water consumption and handling conducted in thermal in situ bitumen recovery processes, improving overall efficiency by lowering energy requirements and environmental impact of these production techniques is a priority. Steam Assisted Gravity Drainage (SAGD) is a common thermal technique used in Athabasca reservoirs. Although SAGD is effective at producing bitumen, its energy efficiency can be poor due to enormous heat losses on the surface and in the wellbore. Given current attention to carbon dioxide emissions and water handling, there is a need to design and implement Reduced Emissions to Atmosphere Recovery (REAR) processes for heavy oil and bitumen extraction. One alternative is to generate steam in situ by in situ combustion (ISC) by injecting air or oxygen into the formation thus reducing or even avoiding transfer heat losses. In ISC, an energy generating oxidation zone propagates within the formation and generates heat which enables in situ steam generation from formation and injected water within the reservoir. In this research, design and optimization of hybrid in situ steam generation recovery processes are examined by using advanced three-dimensional reactive thermal reservoir simulation. Hybrid techniques combine the advantages of both ex situ steam and in situ steam generation processes in that it raises overall energy efficiency, lowers natural gas consumption as fuel, reduces overall gas emissions as well as water usage to generate steam, all on a per unit oil basis. The research here identifies steam-air based hybrid processes that use roughly 70% of the energy of conventional SAGD to recover the same amount of oil with substantial reduction of flue gas emissions and water use as viable REAR processes worthy of scaled physical model and potentially field testing. Introduction Two requirements of in situ heavy oil recovery processes must be met for successful performance: first, make the heavy oil or bitumen mobile, and second, move the mobilized oil to a production well. Since virgin viscosities of Athabasca bitumen generally exceed several million cP, especially near the base of the oil leg (Larter et al., 2006; Gates et al., 2008), the key to success is first measured by the process' ability to mobilize the heavy oil or bitumen. There are four main methods to accomplish this: heat, solvent dilution, solvent deasphalting, and in situ upgrading. An efficient source of heat is in situ combustion (ISC) which also provides combustion gases that could potentially aid movement of mobilized bitumen in the reservoir.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".