Optimization of a Closed-Loop Geothermal System Under Different Operational Conditions
Bibliographic record
Abstract
Abstract Geothermal energy represents a promising source of power with the potential to significantly contribute to global energy needs. Closed-loop geothermal system is a technology designed to maximize extraction of energy from a geothermal resource while minimizing environmental impact. The closed-loop configuration circulates a heat exchange fluid through an isolated wellbore within the underground geothermal reservoir. Different reservoir conditions, wellbore configurations and operational conditions introduce unique challenges and opportunities for harnessing this vast energy resource efficiently. The objective of this work is to simulate and evaluate the geothermal energy potential of a closed-loop geothermal system under different operational conditions. The study focuses on a horizontal well with varied conditions such as reservoir temperature gradient, reservoir thermal conductivity, tubing thermal properties and operational conditions. The proposed well configuration is modeled using a mechanistic transient wellbore tool coupled to a numerical reservoir simulator to assess the circulation of water through the annulus-tubing coaxial loop. The process efficiency is evaluated through analysis of maximum attainable flow rates, temperature, net enthalpy, as well as the net thermal power. Once the ideal configuration has been determined, further optimization is carried out to determine optimal condition through different operational conditions. Simulations are performed involving varied injection rates, injection temperatures, maximum wellhead injection pressures, tubing insulation length, and tubing dimensions to identify the most efficient case on generating highest net thermal power. The findings suggest that the efficiency of a closed-loop geothermal system depends on several variables, including the reservoir's temperature gradient, thermal conductivity of the reservoir rock, and wellbore as well as operational conditions such as injection temperature, maximum wellhead injection pressure, and completion design (insulation extension to the horizontal section and wellbore length). The process has found to be more efficient in reservoirs with a high temperature gradient and thermal conductivity particularly when employing lower injection temperatures. Moreover, increasing wellbore length (contact area) could further enhance the thermal efficiency by improving the conduction mechanism. Further optimization of completion design reveals that circulating a greater volume of water can be achieved through a larger tubing and higher injection pressure, but with only a slight increase in net thermal power. Overall, the identified factors influencing efficiency, such as reservoir temperature gradient, reservoir thermal conductivity, wellbore contact area, and injection temperature have found to be the most impactful parameters on the optimal operation of a closed-loop geothermal system. The outcomes of this research provide valuable insights into the optimal design and operation of closed-loop geothermal systems under different reservoir and operational conditions. The knowledge gained from this study has the potential to enhance the sustainable utilization of geothermal energy.
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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 | 0.000 |
| 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) | 0.003 | 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".