Thermo-Poro-Mechanical Analysis of the Effects of Low-Temperature CO2 Injection on Caprock Integrity
Bibliographic record
Abstract
Abstract For any CO2 sequestration project, a competent caprock acts as the primary and immediate trapping mechanism that prevents the injected fluid to leak out of the reservoir. This paper reviews the results of a thermo-poro-mechanical model developed to study the effects of low-temperature CO2 injection on the caprock integrity for a hypothetical CO2 storage project. A detailed study was conducted to characterize the behaviour of the model and to identify the influence of interactions between fluid flow, heat transfer, and geomechanics in the model. The results show that pressure and temperature distributions in the model are dissimilar and, consequently, their effects on the geomechanical response of the model are significantly different. It was observed that the low temperature of CO2 has a dominant effect on the geomechanical response of the model. In general, a temperature decrease leads to severe reduction in the values of in-situ stresses and, consequently, it increases the potential of fracturing in the aquifer and the caprock. Nevertheless, this potential is smaller in the caprock than in the aquifer as a result of a lower temperature decrease in the caprock. This contrast is accentuated by the pore pressure drop caused by the temperature decrease in the low-permeability caprock with undrained flow behaviour. It is also shown that the behaviour of the model may be approximated by using analytical solutions. The paper also reviews the results of a series of analyses conducted to evaluate the sensitivity of tensile fracturing potential in the caprock to several different modeling parameters. These sensitivity analyses showed that the variation in some parameters such as Poisson’s ratio, heat capacity, thermal conductivity, relative permeability, and permeability of the underburden have modest effects on the value of effective stresses in the caprock, while variations in Young’s modulus and the thermal expansion coefficient can significantly influence these stresses and the potential for tensile fracturing in the caprock. Sensitivity analyses also show that injection of CO2 with a temperature closer to the temperature of the aquifer reduces the potential for tensile fracturing in the caprock and aquifer to a great extent. The potential of fracturing in the caprock is also highly reduced by injecting CO2 at greater depths within the aquifer and farther away from the caprock.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".