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[{"model": "core.projectfund", "pk": 25258, "fields": {"project": 2445, "organisation": 5, "amount": 82180, "start_date": "2018-01-01", "end_date": "2019-03-31", "raw_data": 39791}}]
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[{"model": "core.projectfund", "pk": 17361, "fields": {"project": 2445, "organisation": 5, "amount": 82180, "start_date": "2018-01-01", "end_date": "2019-03-31", "raw_data": 10684}}]
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[{"model": "core.projectorganisation", "pk": 65101, "fields": {"project": 2445, "organisation": 147, "role": "PP_ORG"}}]
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[{"model": "core.projectorganisation", "pk": 65100, "fields": {"project": 2445, "organisation": 2971, "role": "PP_ORG"}}]
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[{"model": "core.projectorganisation", "pk": 65099, "fields": {"project": 2445, "organisation": 2972, "role": "PP_ORG"}}]
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[{"model": "core.projectorganisation", "pk": 65098, "fields": {"project": 2445, "organisation": 291, "role": "PP_ORG"}}]
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[{"model": "core.projectorganisation", "pk": 65097, "fields": {"project": 2445, "organisation": 58, "role": "PP_ORG"}}]
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[{"model": "core.projectorganisation", "pk": 65096, "fields": {"project": 2445, "organisation": 570, "role": "PP_ORG"}}]
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[{"model": "core.projectorganisation", "pk": 65095, "fields": {"project": 2445, "organisation": 209, "role": "COLLAB_ORG"}}]
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| April 11, 2022, 3:45 a.m. |
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[{"model": "core.projectorganisation", "pk": 65094, "fields": {"project": 2445, "organisation": 570, "role": "COLLAB_ORG"}}]
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| April 11, 2022, 3:45 a.m. |
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[{"model": "core.projectorganisation", "pk": 65093, "fields": {"project": 2445, "organisation": 514, "role": "LEAD_ORG"}}]
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| April 11, 2022, 3:45 a.m. |
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[{"model": "core.projectperson", "pk": 40121, "fields": {"project": 2445, "person": 3361, "role": "RESEARCH_COI_PER"}}]
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| April 11, 2022, 3:45 a.m. |
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[{"model": "core.projectperson", "pk": 40120, "fields": {"project": 2445, "person": 3365, "role": "RESEARCH_COI_PER"}}]
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| April 11, 2022, 3:45 a.m. |
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[{"model": "core.projectperson", "pk": 40119, "fields": {"project": 2445, "person": 440, "role": "PI_PER"}}]
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| April 11, 2022, 1:47 a.m. |
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{"title": ["", "Improving marine growth estimates using 3D photogrammetry"], "description": ["", "\nOffshore infrastructure, including oil and gas and renewables installations, are rapidly colonised by a diverse range of animals and plants (seaweeds), here referred to as 'marine growth'. The nature and extent of marine growth has both engineering and ecological consequences for the performance and integrity of offshore infrastructure. From an engineering perspective, marine growth changes critical structure characteristics affecting performance at both the operational (e.g. drag and loading forces) and decommissioning phases (e.g. jacket lifting/towing). From an ecological perspective, marine growth is the basis for the 'reef-effect' offered by offshore structures; marine growth provides ecosystem services such as water filtration, food provision and shelter (e.g. for commerical fish species). \n\nThe scale of offshore energy, and the associated installation and removal of structures, is considerable and includes the commissioning of new wind-farms (up to 50,000 wind-turbines are projected by 2050 in Europe) and decommissioning of oil and gas structures (>20% of North Sea assets to be decommissioned within 10 years). \n\nIndustry needs to monitor four aspects of the marine growth (MG) on their installations. These aspects are marine growth type (e.g. mussels, coral, anemones) mass, volume and surface roughness. Accurate estimation of these aspects is required in order to (1) inform engineering decisions that account for effects of marine growth, (2) optimise cleaning regimes and planning, (3) inform lifting operations at decommissioning and (4) organise disposal of the marine growth. Marine growth estimates are also required to understand the ecosystem-services offered by offshore structures and thus the environmental consequences of installing and removing infrastructure. This information is also required by regulators and policy-makers as an evidence base to optimise decision-making with respect to consenting offshore activities. \n\nCurrently, industry employs a simple algorithm to estimate marine growth thickness and mass on their structures. This algorithm frequently results in substantial overestimates (up to a factor of 20) between predicted and reported MG mass. The current poor MG estimates result in a high degree of uncertainty, for example in the equipment necessary to lift a structure, and this uncertainty incurs considerable costs. \n\nOur project will build on on-going research to assess the feasibility of generating, and analysing, 3D images derived from video footage obtained using remotely operated vehicles (ROV). We will calibrate the MG volume estimated from the ROV-3D images against different MG categories (seaweed, hard-growth, such as mussels, and soft-growth, such as sponges and anemones). We will then take ROV footage gathered around oil and gas structures that have subsequently been decommissioning, and cleaned, and compare our new MG estimates against those recorded by the decommissioning yard. This feasibility assessment will culminate in the production of best-practice guidelines to industry for optimal methods to generate and use 3D images in assessing MG. The knowledge embedded in end-users organisations, as result of this project, will steer adoption of 3D imaging as a novel marine growth assessment tool.\n\n"], "extra_text": ["", "\n\nPotential Impact:\nThe expected outcomes of the project are:\n1. A best-practice guide to industry/consultancies with detailed methodology for optimising the collection of new ROV footage to meet existing engineering requirements and new marine growth assessments. The guide will include recommendations for subsequent data analysis.\n2. An assessment of the feasibility of applying of SfMP techniques to existing ROV footage to generate 3D images of marine growth. \n3. Embedding knowledge of the technique with regulators, industry and consultants through production of trade-journal publications and dissemination through industry bodies (via conference presentations, Tethys Annex IV database for renewables).\n\nThe benefits of these outcome for project partners, and the wider industry sector and policy makers are listed below:\nBenefit 1: Improved mass estimates represent an avenue for significant cost reduction to industry and the government (liable for up to 70% of decommissioning costs and currently subsidising the offshore wind industry). This will result from (1) more informed selection of marine growth removal strategies and lifting/vessel technology during decommissioning, (2) optimising cleaning regimes to minimise disruption to energy generation and (3) informing the design of new infrastructure to account for the damaging consequences of marine growth (abrasion, component wear).\nBenefit 2: Involvement of regulators will ensure that the developed technique and outputs are tailored to end-user needs in assessing environmental interactions (e.g. during the decommissioning comparative assessment process) and in relation to waste disposal. \nBenefit 3: Utilisation of existing or routinely collected industry data (i.e. ROV footage) for marine growth characterisation will provide operators with additional value and return from pre-existing investments in integrity monitoring.\n\n\n"], "status": ["", "Closed"]}
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| April 11, 2022, 1:47 a.m. |
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{"external_links": [8766]}
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| April 11, 2022, 1:47 a.m. |
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[{"model": "core.project", "pk": 2445, "fields": {"owner": null, "is_locked": false, "coped_id": "baeba818-2a34-4f0f-82c9-4f01939d9743", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 10668, "created": "2022-04-11T01:33:55.516Z", "modified": "2022-04-11T01:33:55.516Z", "external_links": []}}]
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