Quantifying Gas Storage and Transport in an Intact Core Sample Using Low-Field NMR and Pressure Measurements
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Quantifying gas movement through low-permeability rocks is important for shale gas resource evaluation as well as characterizing cap rocks for reservoirs storing natural gas, carbon dioxide, and hydrogen gas. In this study, we used an established NMR-based method to determine the ethane gas adsorption isotherm for an intact core plug of Bakken shale, with measurements carried out at 0.3, 1.4, 2.8, and 4.2 MPa. The results showed that there was ten times more ethane mass adsorbed to the sample compared to the ethane mass in the pores at 4.2 MPa. Next, we expanded the methodology to take advantage of the steady state between the core plug and gas in the NMR sample chamber at 4.2 MPa. The chamber was rapidly degassed to a gauge pressure of 0.061 MPa and shut-in. NMR measurements were used to quantify the gas adsorbed to the rock and the gas in the pores of the rock, while gas pressure measurements were made until the pressure reached a new steady state of 0.14 MPa after 21 h. The NMR measurements showed that one-third of the ethane measured at 4.2 MPa remained sorbed to the rock, indicating two-thirds of the ethane had desorbed. A numerical model of the degassing core was created by using TOUGH2 to simulate the desorption of ethane from the core and the movement of ethane out of the core by advection and diffusion. The model confirmed the affinity for ethane to remain adsorbed to the rock even when the pressure was lowered, suggesting enhanced gas recovery techniques may be required to remove a larger proportion of adsorbed gas in tight shale formations. The understanding of adsorption/desorption, permeability, effective porosity, and diffusion coefficient can be applied to reservoir models at reservoir conditions to improve estimates of gas recovery in tight rock reservoirs.
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".