Multiscale Pore-Structure Characterization by Combining Image Analysis and Mercury Porosimetry
Bibliographic record
Abstract
Multiscale Pore-Structure Characterization by Combining Image Analysis and Mercury Porosimetry H. Han; H. Han U. of Waterloo Search for other works by this author on: This Site Google Scholar M. Ioannidis; M. Ioannidis U. of Waterloo Search for other works by this author on: This Site Google Scholar M. B. Dusseault; M. B. Dusseault U. of Waterloo Search for other works by this author on: This Site Google Scholar B. Xu B. Xu U. of Waterloo Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Europec/EAGE Annual Conference and Exhibition, Vienna, Austria, June 2006. Paper Number: SPE-100353-MS https://doi.org/10.2118/100353-MS Published: June 12 2006 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Han, H., Ioannidis, M., Dusseault, M. B., and B. Xu. "Multiscale Pore-Structure Characterization by Combining Image Analysis and Mercury Porosimetry." Paper presented at the SPE Europec/EAGE Annual Conference and Exhibition, Vienna, Austria, June 2006. doi: https://doi.org/10.2118/100353-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Europec featured at EAGE Conference and Exhibition Search Advanced Search Abstract This article introduces a multiscale pore structure characterization method using a combination of mercury porosimetry and image analysis. The method was used to determine the distribution of pore volume by pore size and to estimate the pore-to-throat size aspect ratio. The key idea of the method is that pore size distribution obeys a fractal scaling law over a range of pore size. On this basis, scattering intensity data computed from the measured two-point correlation function and those measured from mercury porosimetry are extrapolated in the size range 0.01 μm < r < 1000 μm, using the known fractal scaling law.A set of siltstone samples taken from Daqing Oilfield was analyzed through this method. Distribution of pore volume by pore size over the entire range of pore length scales was determined. The results demonstrated significant similarities in the pore structure of all samples. The image analysis results were in qualitative agreement with the results of mercury intrusion/extrusion tests.The results were also compared with some other samples (including siltstone, sandstone, and dolomite) that had been analyzed using similar methods. It is shown that the surface fractal dimension obtained by analysis of MIP data is consistent with the value obtained by image analysis for different samples with different porosity and permeability.Novel information on the pore-to-throat aspect ratio is obtained by comparing the complete pore volume distribution (PVD) to the MIP data. Keywords: Artificial Intelligence, Reservoir Characterization, Fluid Dynamics, pore structure, Upstream Oil & Gas, porosity, surface fractal dimension, mip data, length scale, information Subjects: Reservoir Characterization, Reservoir Fluid Dynamics, Formation Evaluation & Management, Information Management and Systems, Flow in porous media, Artificial intelligence Copyright 2006, Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".