In-situ hydrogen production from petroleum reservoirs and the associated high temperature hydrogen attack: A review
Bibliographic record
Abstract
The oilsands gasification and electromagnetic assisted catalytic heating techniques provide promising avenues for in-situ hydrogen production from petroleum reservoirs . A multitude of chemical reactions, such as pyrolysis , aqua-thermolysis, and low-temperature, medium-temperature and high-temperature oxidizations , occur during hydrogen production . The combustion of hydrocarbons, coke gasification, and the water-gas shift reaction are the primary mechanisms for producing hydrogen . The temperature and pressure conditions within the oilsands reservoir vary from 210–250 °C and 2.5–3 MPa to 350–990 °C and 3–10 MPa. Steel facilities subjected to prolonged exposure to high-temperature and high-pressure hydrogen (H 2 ) gas environments are susceptible to high-temperature hydrogen attack (HTHA). In these environments, hydrogen (H) atoms can be produced from molecular H 2 through an adsorptive dissociation mechanism. HTHA is the primary form of damage to steels upon permeation of H atoms. It can result in decarburization of the steels, a decrease in their strength and toughness, and accelerated creep crack growth . While multiple factors affect the HTHA occurrence, a variety of methods can be used to address the problem. Particularly, alloying treatment of carbon steel by elements such as W, Zr, Cr, Mo, V, Ti and Nb can form stable carbides and improve resistance of the steels to HTHA, offering a viable strategy to mitigate the risks associated with HTHA while ensuring cost-effectiveness effectiveness. High-alloy steels with enhanced stability of carbides are recommended for in-situ hydrogen production in petroleum reservoirs, although the steels’ long-term performance in the varying and extreme environments is to be further investigated and modeled.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".