Powering Offshore Installations with Wind Energy
Bibliographic record
Abstract
Abstract The offshore Oil and Gas industry continues to explore and develop oil and gas fields using installations powered by generators burning either natural gas or marine gas oil (MGO), similar to diesel. There is increasing pressure on the industry to explore and produce hydrocarbons responsibly, cost effectively and with the lowest emissions. By employing renewable sources of power, Waterford Energy Services Inc.'s (WESI) methodology provides a possible solution using Floating Offshore Wind Turbine (FOWT) to power offshore installations. Various methods are being proposed and developed to maintain production while bringing down overall greenhouse gas emissions (e.g. CO2, NOx, SOx). This paper outlines the methodology used for a conceptual design of a "Plug and Play" hybrid power solution in the Canadian Offshore Oil and Gas Industry. FOWTs are electrically connected to offshore installations such as Mobile Offshore Drilling Units (MODU), Floating Production Storage and Offloading (FPSO) and fixed production platforms in a harsh environment to replace large portions of the onboard power generation. Battery Energy Storage Systems (BESS) are incorporated to transition from wind power, increase efficiency, provide safety backup and enhance emissions reduction. The plan includes considerations for the optimal electrical and battery storage topology and the electrical equipment necessary to connect the FOWT Array to the offshore facility. For the purposes of this paper, a representative location was chosen offshore Newfoundland and Labrador, Canada to assess both local conditions and design requirements. WESI has evaluated additional global locations for this FOWT solution. The conceptual design considers the wind turbine power output and examines the components required to deliver the power to the installation's electrical system (e.g. transformers, batteries, switchgear, static and dynamic cables, disconnects, communications/monitoring and required safety systems). Although there have been incremental improvements in emissions via advancements in fuel standards and engine exhaust abatement modifications, the only approach to have a significant step-change improvement in emissions is through replacement of onboard power generation with renewable sources. It anticipated that the installations’ greenhouse gas (GHG) emissions can be reduced in excess of 70% by combining wind power and battery supplementation. Oil and Gas operations are ideal applications of this decarbonization approach, presenting an opportunity to mature FOWT technology which can readily adapt to other grid isolated applications such as Remote Communities, Aquaculture and near-shore Industries.
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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) | 0.000 | 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".