Determination of Particle Shape and Size Distribution from Micro X-Ray CT Scans for Petrophysical Evaluation and Sand Control Design
Bibliographic record
Abstract
Abstract Particle size, shape and mineralogy are considered as primary characteristics of sand and sandstone. Several techniques have been developed for the particle size and shape analysis of unconsolidated sands. However, few of these techniques can be used for sandstones. Most particle size measurement techniques provide a spherical equivalent of the particle size and neglect the particle shape. Although several techniques have been developed for the particle size and shape analysis of the unconsolidated sands, these techniques could not be used for the size and shape variation analysis of consolidated or semi-consolidated sandstone. Recently, X-ray micro CT scanning technique has been used for the evaluation of petrophysical properties of sandstones. This paper presents a workflow for the measurement of particle size and shape of sandstones. This research utilized X-ray micro CT scans for 2-dimensional particle shape measurements including Sphericity, Convexity, Aspect Ratio and Feret diameters. The methodology presented in this paper is the first step toward assessing the particle shape and size variation of sandstones for use in such applications as sand control design. Image-J, an Open-source software, was used to process and filter the X-ray raw images. A new tool was developed to measure the shape factors (i.e. Sphericity, Aspect Ratio and Convexity) and size variations. A series of images from different sandstones were analyzed and compared to their lab measurements. The image calculated porosity and permeability showed some degree of deviation from the lab measured porosity and permeabilities. This paper presents a new workflow to measure the particle size and shape for the sand control design in sandstone reservoirs. With a larger database it is possible to develop a correlation to calculate rock properties from image size analysis technique and correct them for the shape variation. The next step will be to measure the 3D size and shape from the image analysis and compare to the shape and size analysis from dynamic image analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".