Petrophysical properties of representative geological rocks encountered in effective carbon storage and utilization
Bibliographic record
Abstract
Under studies for nearly 20 years, several different kinds of subsurface lithologies, such as deep saline aquifers, depleted oil and gas reservoirs, non-minable coal seams, geothermal reservoirs, organic-rich shale, and basalt, can be candidates for permanent CO 2 geological storage processes, in a broad context of carbon capture, utilization, and storage (CCUS).These systems commonly need effective reservoirs (sandstones, carbonates) for large-volume storage (e.g., effective porosity) and impermeable cap rock (mudrocks and salt rock) for containment (e.g., permeability, diffusivity).This work studies 10+ representative rock samples from typical geological formations encountered at CCUS, such as Berea sandstone, Crab Orchard sandstone, Guelph dolomite, and Indiana limestone (as depleted sandstone or carbonate oil/gas reservoirs & saline aquifers) with Woodford claystone and Himalayan salt rock as cap rocks.Haynesville Shale, Sihe coal, Texas basalt, and Sierra white granite were also used to study the storage and utilization in shales, coal seams, and basalt formations, as well as enhanced geothermal systems.The important petrophysical attributes (properties of rocks and fluids, as well as fluid-rock interactions) for this wide range of geological rocks are not available or sufficiently studied with respect to different methodologies, vast lithological difference, and sample scale effect, with a particular focus on how microscopic pore structure (especially pore connectivity) influences macroscopic fluid flow and chemical transport [1].In conjuction with a set of complementary approaches for pore structure characterization (such as small angle neutron/X-ray scattering), this work utilizes several custom designed apparatuses (e.g., gas diffusion) to provide the essential information of CO 2 diffusivity and tortuosity of natural rocks, in the presence of other gases (CH 4 , H 2 , and O 2 ), in assessing the effectiveness of CCUS in typical gological formations [2].
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".