Application of Raman spectroscopy for analyzing the behavior of gases in sandstone reservoirs
Bibliographic record
Abstract
• A novel method to observe the behavior of gases in sandstone-reservoir samples using a Raman spectroscopy system was developed. • A pressure-cell was designed and manufactured to inject gases into the reservoir sample. • Two-dimensional and three-dimensional mapping of the pores depicted the geometry of the CH 4 and CO 2 gases-filled pores. • The results can serve as a foundation for understanding the behaviour of gases in subsurface reservoirs. The behavior of gases within subsurface pores determines the oil and gas recovery and CO 2 storage in the region. In this study, we report a novel method based on Raman spectroscopy for observing the distributions of CH 4 and CO 2 gases in the pores of sandstone reservoirs. First, we designed a pressure-cell to inject gases into a sample. Then, CH 4 and CO 2 gases were injected into the sample using the pressure-cell placed on the Raman spectroscopy system sample stage. Quartz and feldspar were the predominant minerals in the sample. The CH 4 -occupied pores exhibited a Raman peak at 2917 cm −1 . Two-dimensional (2D) and three-dimensional (3D) mapping of the pores depicted the geometry of the CH 4 gas-filled pores. After injecting CO 2 gas, we observed an intensity peak at 1388 cm −1 ; we obtained 2D and 3D maps of the CO 2 gas-filled pores based on this peak value. This study demonstrates the potential use of Raman spectroscopy as a visualization tool to reveal the pore geometry of sandstone reservoirs and determine the distribution of gases within such reservoirs. Our study can serve as a foundation for understanding the behavior of gases in subsurface reservoirs, improving oil and gas prospecting and exploration, and assessing CO 2 storage. .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".