Methane Isotherms and Magnetic Resonance Imaging in Shales
Bibliographic record
Abstract
Adsorption isotherms of light hydrocarbons on reservoir rocks are key data used to quantify the total gas content in reservoirs and isotherms are now being used to improve our understanding of the processes affecting subsurface gas flow associated with gas injection from Enhanced Oil Recovery techniques. This project combined elements of the traditional pressure-volume gas adsorption isotherm technique and an NMR-based adsorption isotherm approach to determine the adsorption isotherms of light hydrocarbons on to tight rocks from oil and gas reservoirs. The new approach allows isotherms to be derived from NMR data. First, a T 2 distribution of the gas is determined over a range of gas pressures. Next, the volume of pore gas is estimated using the pore volume of the rock and the Van der Waals gas equation. The adsorbed gas content is then calculated by subtracting pore gas content from the total gas content. This is repeated for a range of gas pressures to determine the adsorption isotherm. This project used the NMR method described above and measured the gas pressure decay in the NMR cell. This combined approach includes the advantages of the NMR method but it also produces a pressure-time curve that can be used to identify when equilibrium is attained in low permeability rocks and can be used to compare adsorption kinetics of different gases. The advantages of our approach are that 1) the samples remain intact and the measurements provide information on the pore size distribution; 2) analyses can be carried out at reservoir pressures; 3) isotherms can be measured for any gas containing hydrogen atoms; and 4) the results can be used to examine the processes controlling gas flow through the rock. Future work to develop this technique will improve our quantification of the amount of pore gas in the cell, which will improve our partitioning between adsorbed gas and pore gas as well as allow for an improved analysis of the pressure response of the sample after degassing.
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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) | 0.000 | 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".