Potential analysis of in-situ hydrogen generation in narrow reservoir with alternating steam-oxygen injection
Bibliographic record
Abstract
The growing demand for carbon-neutral energy has driven advances in clean energy utilization and hydrocarbon conversion. This study introduces a novel in-situ hydrogen generation method using steam–oxygen alternating injection and high-temperature thermal reactions within a heterogeneous reservoir model. The workflow integrates WinProp/CMG thermal–reaction simulation with three sub-models that capture porosity–permeability contrasts, saturation variations, and fault barriers. Before gas breakthrough, the system maintains 600–800 ℃ with a hydrogen yield of 1781.3 m 3 per m 3 of fossil fuel and 34.94 % oil recovery. Sub-model 1 shows that fault-induced breakthrough delay extends the hydrogen-generation period, reaching a peak of 2221.7 m 3 at approximately 700 ℃. A categorical operating window emerges: below 600 ℃ favours combustion-dominated reactions, while above 800 ℃ accelerates coke oxidation and suppresses H 2 yield. After a breakthrough, continued O 2 injection lowers temperature and sharply reduces productivity, emphasizing the importance of thermal management and cycle timing. Compared with conventional underground coal gasification, alternating injection improves hydrogen recovery by 8.8 % and reduces greenhouse gas emissions by 8.2–15.6 % by suppressing H 2 and CO combustion. 3D simulations reveal conical cavity growth driven by pressure gradients and fault-moderated continuity, supporting the feasibility of screening for unconventional settings.
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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 | 0.000 | 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".