Replication of Carbonate Reservoir Pores at the Original Size Using 3D Printing
Bibliographic record
Abstract
Three-dimensional (3D) printing is a powerful tool that enables visualization, replication, and experimentation with natural porous rocks. Over 100 years, natural rocks have been a focus of studies on how fluids such as hydrocarbons, greenhouse gases, and water flow through their porous systems. Scale and resolution are among the most challenging factors for current 3D printing methods when attempting to replicate the pore architecture of natural porous media. Most 3D printing techniques have resolution restraints during fabrication that makes feature reproduction at the 1:1 scale almost impossible. A new developing technology that uses two-photon lithography and ultraviolet (UV) light curable resin allows for nanometer features to be 3D printed. However, the main challenge of this 3D printing method is the small size of the resulting model (less than 20 mm in each direction). This technical note presents a detailed workflow on how to fabricate a carbonate rock replica at the micron scale. To test this workflow, a pore network was obtained from tomographic data of a reservoir rock core located in Mexico (1 mm in diameter and 2 mm in height) and was 3D printed at the original size. This replica was subjected to tomographic and scanning electron imaging to verify the accuracy of pore geometry. Incorporating lithographic printing into novel rock experiments that concern multiscale, multiphysics models of fluid flow and deformation open an 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 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 | 0.002 | 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 | 0.000 | 0.001 |
| 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".