Hydrogen Generation from Heavy Oils via In-situ Combustion Gasification
Bibliographic record
Abstract
Abstract In-situ combustion (ISC) is a technology used for enhanced oil recovery for heavy oil reservoirs. In two ISC field pilots conducted in 1970s to 1980s in Canada, 10-20% mole fraction of hydrogen (H2) was produced accidentally. This presents a potential opportunity for petroleum industry to contribute to the energy transition by producing hydrogen directly from petroleum reservoirs. However, most ISC experiments have reported no or negligible hydrogen production, and the reason remains unclear. To address this issue, this study focuses on hydrogen generation from bitumen through in-situ combustion gasification (ISCG) at a laboratory scale. CMG was used to simulate the ISCG process in a combustion tube. Kinetics from previous ISC experiments and reactions for hydrogen generation were incorporated in the models. Heavy oil, oxygen, and water were simultaneously injected into the tube at a certain temperature. The ranges of key parameters were varied and analyzed for their impact on hydrogen generation. The study found that maintaining a temperature above 400 °C is essential for hydrogen generation, with higher temperatures yielding higher hydrogen mole fractions. A maximum of 28% hydrogen mole fraction was obtained at a water-oxygen ratio of 0.0018:0.9882 (volume ratio at ambient conditions) and a temperature about 735 °C. Higher oxygen content was found to be favorable for hydrogen generation by achieving a higher temperature, while increasing nitrogen from 0 to 78% led to a decrease in hydrogen mole fraction from 28% to 0.07%. Hydrogen generation is dominated by coke gasification and water-gas shift reactions at low and high temperatures, respectively. This research provides valuable insights into the key parameters affecting hydrogen generation from bitumen at a lab scale. The potential for petroleum industry to contribute to energy transition through large-scale, low-cost hydrogen production from reservoirs is significant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".