Investigation of Carbonate Rock Thermal Conductivity as a Function of Temperature, Porosity and Fluid Saturation Using a Comparative Approach
Bibliographic record
Abstract
Abstract Carbonate rocks are common formations in hydrocarbon reservoirs, and thermal recovery methods are often employed to enhance production. The success of a thermal project is highly dependent on comprehensive knowledge about the thermal behavior of any involved component. Consequently, the availability of reliable and accurate thermal property data, such as thermal conductivity, improves optimization and operation procedures in these types of operations. Measurement of thermal conductivity of carbonate rock has been a matter of extended research, yet different techniques result in different measurements and the understanding of the effect of elevated temperatures is limited. Prior researchers used transient approaches in the thermal conductivity measurements, which resulted in poor accuracy, despite having low measurement time. Moreover, the thermal conductivity of the saturated carbonate samples has not been investigated, as the existing research mainly focused on dry samples. In this study, first, thermal conductivity is measured of five different carbonate samples with a wide range of effective porosity (from 5 to more than 30 %) using a steady-state approach within a wide range of temperatures (from 40 to 150 ˚C). Then the same procedure was repeated for saturated samples to investigate the effect of saturation in different porosity and temperatures on the thermal conductivity trend and values. Results showed that in the dry samples, there is a downward trend for the thermal conductivity of all five samples as the temperature increased. For samples at similar temperatures, as the porosity of the sample increased, an increase was observed in the thermal conductivity values in dry cases, and for the porosity values above a certain value, it started to go down as we expected, and it was interpreted as the effect of mineralogy which is another crucial parameter beside the porosity in the ultimate thermal conductivity value of a porous medium. We measured effective porosity; however, the total porosity of the sample plays a much more important role in the heat transfer along the sample, and the relationship between these two porosities depends on the samples’ pore connectivity. Thermal conductivity measurement for the saturated cases carried out by a modification in the setup. Results showed a similar trend as the temperature was increased and the values were higher compared to corresponding dry sample which revealed the incapability of averaging methods as a generalized approach for saturated rock sample thermal conductivity prediction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".