Using Gamma-Ray and X-Ray Computed Tomography for Porosity Quantification of Reservoir Analogue Rocks
Bibliographic record
Abstract
During the last few decades, X-ray micro-computed tomography (µCT) has been largely used to characterize rock properties and to create high-resolution 3D digital image volumes. It has allowed access to important information about porous systems in reservoir rocks. However, the reliable quantification of porosity of rocks which present porous volumes ranging from centimeter to nanometer scale remains a challenge. Assessment of nano scale porous volume is very difficult by image segmentation techniques, due to the intrinsic limits of the x-ray imaging method. Moreover, image processing for analysis of various types of porosity in the same sample, including microporosity could be computationally expensive. We present a method based in the Gamma-Ray computed tomography (axis attenuation) that can substantially improve the limits presented by conventional X-ray microtomography. This study compared the porosity values acquired by typical segmentation methods for microtomography images, and by the values obtained trough the proposed method of gamma-ray computed tomography to calculate the porosity. Results of both approaches were compared to porosity measurements obtained through experimental equipment (helium porosimeter). These analyses were performed in core samples of limestones and sandstones analogous of Brazilian oil reservoirs. The Gamma Ray Attenuation method (axis attenuation) presented a better correlation (R² = 0.9588) to the experimental measurements when compared to the image segmentation methods (R² = 0.9194). The results suggest that Industrial application of gamma ray tomography for precise evaluation of large number of core samples can be highly effective. Furthermore, the gamma ray data can be integrated with data provided by conventional µCT image processing to complement information regarding morphological aspects. Keywords: Porous System, X-ray microtomography, Gamma Ray tomography, Reservoir rocks
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".