Transforming Depleted Oil and Gas Wells into Geothermal Energy Assets: A Sustainable Vision for Energy Transition
Bibliographic record
Abstract
Abstract Geothermal energy is increasingly recognized as a vital renewable energy source with significant economic and environmental benefits, contributing to the global transition to cleaner energy. This paper explores the economic feasibility and environmental impact of geothermal energy development, focusing on the high initial costs associated with drilling and infrastructure, which are offset by low operational costs and long-term sustainability. While geothermal energy projects incur substantial upfront investments, particularly in drilling, they offer a reliable and low-maintenance energy solution with operational lifespans of 20 to 50 years. The paper examines the cost-benefit dynamics, comparing geothermal leveled cost of electricity (LCOE) with other renewable sources like wind and solar, demonstrating that geothermal is competitive in regions with high geothermal potential. In addition to cost considerations, the environmental advantages of geothermal energy are discussed, emphasizing its low greenhouse gas emissions and carbon intensity, which make it one of the most sustainable energy sources. The reduction in emissions is crucial in addressing climate change and meeting global carbon reduction targets. The paper also addresses the sustainability of geothermal energy, focusing on the importance of resource management to avoid overexploitation and depletion. Strategies such as fluid reinjection and the development of enhanced geothermal systems (EGS) are highlighted as solutions to ensure the long-term viability of geothermal resources. Furthermore, the paper presents successful case studies from various regions, including California, Alberta, the Philippines, and the United Kingdom, where abandoned oil, gas, and exploration wells have been repurposed for geothermal energy production. These case studies demonstrate the cost-saving and environmental benefits of utilizing existing infrastructure, reducing the need for new drilling and minimizing environmental impacts. By leveraging advanced technologies and innovative approaches, geothermal energy can provide a sustainable, low-carbon energy source that addresses both economic and environmental challenges. The paper concludes by highlighting the potential of geothermal energy to contribute significantly to global energy sustainability, offering a reliable, eco-friendly solution for the future.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".