An in-situ low-carbon enhanced oil recovery approach applied in high viscous oil reservoir
Bibliographic record
Abstract
High heat loss, substantial energy consumption, considerable CO2 emission and low thermal utilization efficiency are main challenges in the thermal-based production methods applied in high viscous oil reservoir. To address these limitations while achieving both high oil recovery and reduced carbon footprint, this perspective systematically investigates an enhanced high viscous oil recovery method that integrates in-situ pyrolysis with downhole electric heater. Laboratory experiments and field applications demonstrate that this novel technology offers multiple advantages over conventional thermal-based methods, such as higher thermal utilization efficiency, lower carbon emissions and reduced energy consumption. In this novel technology, with high temperature in the reservoir, inducing pyrolysis and cracking reactions in high viscous oil, significantly reducing oil viscosity and enhancing oil recovery factor. Thereby, this novel method presents a viable, low-carbon, and efficient pathway for future development of high viscous oil resources. Document Type: Perspective Cited as: Zhou, X., Liu, J., Zhao, Y., Zeng, F., Jiang, Q., Zhang, L. An in-situ low-carbon enhanced oil recovery approach applied in high viscous oil reservoir. Advances in Geo-Energy Research, 2025, 18(3): 291-294. https://doi.org/10.46690/ager.2025.12.08
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".