Repurposing Gulf of Mexico Oil and Gas Facilities for the Blue Economy
Bibliographic record
Abstract
Abstract This paper presents a summary of the results of a study of the potential for repurposing legacy oil and gas facilities in the Gulf of Mexico for Blue Economy uses. It was a limited study designed to summarize practical options for repurposing. The conclusions list those areas where further modeling or studies are warranted and required with the objective being to build an integrated modeling tool to assist companies, government agencies, and NGO's in assessing the benefits and risks for repurposing specific facilities. 30CFR 585J can be used to expedite permitting the deployment of renewable energy in the Gulf of Mexico. This regulation specifically is written to allow addition of renewable energy and other marine activities on to existing oil and gas leases and is written broadly enough it can be applied to other Blue Economy activities. For the transition to occur the current policy of removing non-producing assets must be stopped. These facilities, providing they are still in good repair, can be a tremendous asset that can accelerate the decarbonization of the Gulf Coast Region, and become the basis for an industrial transition that will create more employment and value than even the peak of offshore oil and gas. The Gulf of Mexico has an excellent offshore renewable energy potential. NREL places the total Gross Resource available at 2000GW and the Technical (recoverable) resource at 500GW. Neither value includes geothermal, and because of the conservative way the NREL calculates the resources the actual potential is at least twice as large. The DOE study, summarized in this paper, provides detailed answer to the following questions: What is the potential value of legacy platforms, wells, and pipelines to renewable energy and Blue Economy activities? What renewable energy systems are economically viable in the Gulf of Mexico and can be deployed safely on existing leases? What Blue Economy industries can be facilitated by using legacy assets in the Gulf of Mexico? Who can apply for and what is the process for repurposing existing assets or adding new assets to existing leases? What are the benefits to the current owners, the Gulf Coast Region, and environment? Finally, the paper provides the outline of a process that stakeholders can use to evaluate individual existing facilities for potential use in building a Blue Industrial base in the Gulf of Mexico.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".