Determination of Thermal Conductivity of Carbonate Cores
Bibliographic record
Abstract
Abstract The difficulties of accurately measuring thermal properties, such as thermal conductivity of fractured and/or vuggy rocks are well known. Many commercially available methods are suitable only for liquids or re-packed sands. Others either require samples to be fairly uniform or are potentially destructive due to sample size limitations. In-situ measurements are possible, but can be costly. It can also be affected by in-situ distributions of fluids in the fractures and vugs, such as water, oil and possibly gas. In order to adapt the highly non-uniform nature of the carbonate cores without having to create further destruction of these cores, we developed a non-destructive method for measuring thermal conductivity of highly vuggy and moderately fractured carbonate cores in their whole diameter. In this paper, we report the theoretical background of this methodology; laboratory observations of thermal behaviours; data analysis and resulting thermal conductivity values of carbonates cores. Using this method, we measured 20 cleaned carbonate cores (88 mm in diameter) from Grosmont C and D Formation in Saleski area. Measured thermal conductivity values ranged from 1.00 to 2.87 W/m•K in Grosmont C, and 0.82 to 3.16 W/m•K in Grosmont D. These values were determined to be a strong function of porosity rather than mineralogy, as the Grosmont Formation typically consists of greater than 95% dolomite. These measurements are also shown to be in good agreement with prior studies on non-fractured dolomite reservoirs. A correlation for thermal conductivity was derived which can be used for numerical simulation models.
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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".