Biogenic Gas Generation From Shallow Organic-Matter-Rich Shales
Bibliographic record
Abstract
ABSTRACT Understanding both physical and biological processes of gas generation and movement in immature organic matter rich shales is essential to optimize gas production from this resource. As yet, there is no complete description that accounts for the many gas transport modes in these systems. Field production data reveals that gas production from these reservoirs declines initially and then stabilizes after a specified time. The stabilized rate is controlled by the contributions of biogenic gas generation, desorption of gas from kerogen, diffusion and transport of gas through nanometer to potentially even micron scale pore systems in coarser grained intervals where physics of transport differ in mechanism. One factor that is ignored is the biogenic gas generation rate and whether this is significant in recharging the resource during a resource well lifetime. This paper presents a modified gas material balance to account for biogenic gas generation. The results of the theory are then compared to gas production data obtained from a shallow shale reservoir in Western Canada. Production from biogenic shale gas reservoirs tends to be at low rate but is stable over extended periods of time. However, it is not yet well understood what the dominant gas transport mechanisms are so that production from these reservoirs can be improved and optimized. Although the gas material balance approach is well established, it has not been used to constrain biogenic gas generation rates. The analysis conducted in this research can be used to optimize productivity from these fields. The new theory helps reveal the key physical controls on gas production from organic shales and means to enhance it. The theory developed in this study is novel and significant because it further develops the underlying theory for biogenic gas transport and production from organic shales, a potentially massive gas source in Western Canada and worldwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.011 | 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 teacher head, 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".