Temperature Transient Analysis of Naturally Fractured Geothermal Reservoirs
Bibliographic record
Abstract
Summary A potential approach to develop geothermal energy is by producing low-to-medium temperature fluids from naturally fractured geothermal reservoirs (NFGRs). Pressure transient analysis (PTA) is the most common approach to characterize such reservoirs for improving development efficiency. However, pressure inversion leads to nonuniqueness and cannot be used to estimate thermal properties. Moreover, reliable methods to evaluate the development potential of fractured geothermal reservoirs are lacking. To address the gap, this work aims to study the temperature behavior and explore a suitable analysis method for characterizing geothermal reservoirs and evaluating development potential. We developed numerical and analytical models to analyze the temperature behavior in NFGRs. The developed models account for the Joule-Thomson [J-T effect (μJT)], adiabatic heat expansion/compression effect (ζ), reservoir formation damage, heat conduction, and convection effects. The developed numerical solution is verified and found to agree with the proposed analytical solutions. The results show that temperature transient analysis (TTA) with constant or temperature-dependent μJT and ζ assumption leads to a minor difference when reservoir temperature changes significantly. Moreover, three heat radial flow regimes (HRFR) and a thermal interporosity regime with a V-shape characteristic have been identified. The results also show that temperature data provide information not accessible by PTA. The results reveal that temperature derivative curves signify a “hump” when formation around a wellbore is damaged, and the temperature data can be used to characterize the skin-zone radius and permeability. It is demonstrated that the properties such as J-T coefficient, effective adiabatic heat expansion coefficient, and fracture intrinsic porosity can be estimated using TTA. The results indicate that fracture thermal storativity (ωT) and matrix thermal interporosity coefficient (αT) can be estimated from the thermal interporosity regime exhibited on the temperature derivative curve. The results also suggest that commercial geothermal energy harness is more difficult when the ωT is high or the αT is very small. Finally, we introduced an integrated workflow of combining PTA and TTA to characterize NFGRs. Simulated test examples are interpreted to demonstrate the applicability of the developed workflow. This work aids in better understanding the potentials of temperature data on geothermal reservoir characterization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".