Reservoir Geomechanical Properties Characterization of 3D Printed Sandstone
Bibliographic record
Abstract
Advances in additive manufacturing technology have enabled the use of sand as a 3D printing material. This has allowed 3D printed sandstone analogues to be produced that significantly reduce specimen-to-specimen heterogeneity and conversely, provides exacting control over the explicit inclusion of heterogeneity, such as fractures, within a specimen. By knowing and controlling inter-sample variability in terms of porosity, fracture networks, grain size distribution, and density distribution, 3D printing of geomaterials provides a valuable tool to validate numerical models, develop scaling laws and constitutive relationships, quantify the degree of influence of pore geometry, fracture network characteristics, and structural heterogeneity on macroscopic properties. Extensive research efforts are underway to fully characterize the thermo-hydro-mechanical properties of 3D printed sandstone specimens. Parallel to this study, tests such as shear strength, compressibility, permeability, wettability and pore size distribution have been conducted under various 3D printing configurations such as binder saturation, specimen orientation relative to build layer orientation and layer thickness. A range of testing results with a particular focus on the compressibility characteristics of the specimens are presented in this paper to demonstrate that 3D printed sandstone specimens has the potential to serve as the foundation for the next generation of experimental investigations of multi-scale, multi-physics reservoir geomechanical processes.
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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 | 0.001 | 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) | 0.001 | 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".