Reactive Reservoir Simulation of Biogenic Shallow Shale Gas Systems Enabled by Experimentally Determined Methane Generation Rates
Bibliographic record
Abstract
As conventional gas resources in Canada decline, more interest is being given to unconventional shale gas reservoirs. Natural gas also has the potential to overcome other petroleum sources, such as coal, heavy oil, and conventional oil, as the fuel of choice because it is a cleaner source of energy with lower carbon emissions. As the world slowly shifts toward cleaner energy sources, it becomes increasingly important to study unconventional shale gas reservoirs. Shallow biogenic shale gas reservoirs generate gas by microbial activity, implying that current production to the surface consists of ancient adsorbed gas as well as recent biogenerated gas. Approximately 20% of all of the methane generated is generally thought to be of microbial origin. Most shallow shale gas reservoirs are at temperatures of less than 80 °C, and given the supply of carbon, water, and minerals, they can be thought of as multi-kilometer-scale bioreactors. In this study, the reaction rate kinetics for methane production were determined from experimental data using produced water and core samples from a shallow shale gas reservoir. These data, together with Langmuir desorption data, were used to model a heterogeneous shale gas reservoir using reactive reservoir simulation. The results show that biogenic shale gas generation accounts for about 12% of the total gas produced during a period of 2678 days. This is a significant percentage of the total gas production, and therefore, there is great potential to enhance methanogenesis within these reservoirs, because there are a number of methods to enhance microbial activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".