Replication of Carbonate Reservoir Pores at the Original Scale Using 3D Printing
Bibliographic record
Abstract
Summary 3D printing is becoming a powerful tool to visualize, reproduce, and experiment with porous media. Natural rocks are part of porous media that have always been a focus of studies on how fluids, such as hydrocarbons, greenhouse gases, and/or water, flow through porous systems. Scale and accuracy are among the most challenging factors for current 3D printing techniques when attempting to replicate the pore architecture of natural porous media such as rocks. However, current 3D printing techniques have resolution restraints during fabrication that make feature reproduction at the 1:1 scale almost impossible. A new emerging technology that uses two-photon lithography and ultraviolet-light curable resin allows for microscopic features to be resolved during fabrication. To test this technology, a pore network was obtained from tomographic data of a reservoir rock sample in Mexico (1 mm in diameter and 2 mm in height) and was 3D-printed at the original scale. The 3D-printed sample was subjected to optical and electron imaging to verify the accuracy of pore geometry. Incorporating lithographic printing into novel rock experiments that concern multi-scale, multi-physics models of fluid flow and deformation open unprecedented opportunity for more controlled prediction of reservoir fluid dynamics, carbon capture and storage, and continuum mechanics.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".