SWOT analysis for geothermal energy extraction from legacy oil & gas wells – critical review
Bibliographic record
Abstract
The global imperative towards sustainable energy development and the initiatives towards NETZERO have intensified research into unconventional energy resources. In this regard, the untapped potential of geothermal energy can be harnessed either conventionally or by repurposing the near-decommissioned hydrocarbon wells. This study analyses the Strengths, Weaknesses, Opportunities, and Threats (SWOT) associated with geothermal energy development. A qualitative analysis of various aspects such as (i) technical intricacies, (ii) environmental considerations, (iii) economic viability, and (iv) policies of repurposing the near-decommissioned hydrocarbon wells for geothermal heat has been performed. The technical aspects, namely, operational and reservoir conditions, and the heat extraction methodologies play a significant role in the feasibility of extracting geothermal heat. Also, the environmental considerations and economic viability encompass mitigating the cardon dioxide emissions and providing socio-economic benefits in the form of reduced upfront investment and operational costs. In addition, the utilisation of existing well infrastructure aligns with the UN Sustainable Development Goals and offers a feasible pathway to reduce the carbon footprint. Furthermore, by integrating technical insights with the environmental and economic attributes, this study contributes to the evolving discourse on sustainable energy transitions and offers a roadmap for unlocking the vast geothermal potential within existing oil and gas infrastructure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".