Geomechanical laboratory testing for sand production characterization using 3D-printed core analogues
Bibliographic record
Abstract
Abstract Among others factors, the triggers for sanding include: drilling, cementing, and completion operations; stress and pressure conditions; formation strength and weakening; and cyclic processes. Any single factor may suffice to initiate sand production. Hence, comprehending sanding entails examining various physical mechanisms concerning the interaction between fluids and rocks under reservoir conditions. This article presents an innovative study focused on evaluating and understanding the effect on sanding for the following components: vertical to hotizontal stress anisotropy, stresses around the hole, and formation strength and weakening. It combines three points in the analysis: (1) The 3D printing technology, which allows obtaining similar samples with high repeatability, giving reliability to the results; (2) A comprehensive program for the mechanical characterization of printed samples; and, (3) The study of the mechanical behavior of the samples under sanding conditions. The testing program includes uniaxial compressive strength (UCS), triaxial stress test, thick-walled cylinder and big hollow cylinder test with Sanding (BHCT) tests, which aims to show the relationship between the mechanical behavior, the test conditions, and the sand production level. The sample’s characterization reveals a high porosity, the presence of bedding planes, and similar Young’s modulus and UCS strength. For the BHCT tests, a novel equipment is introduced. This equipment uses large samples and enables: an independent control of axial stress, radial stress, pore pressure, and flow rate; and measurement of produced sand. The results show higher levels of sanding when the axial stress is low compared to the radial external stress.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".