| Feb. 13, 2024, 4:20 p.m. |
Created
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[{"model": "core.projectfund", "pk": 67629, "fields": {"project": 15887, "organisation": 5, "amount": 100283, "start_date": "2017-02-01", "end_date": "2018-01-31", "raw_data": 189197}}]
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| Jan. 30, 2024, 4:25 p.m. |
Created
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[{"model": "core.projectfund", "pk": 60450, "fields": {"project": 15887, "organisation": 5, "amount": 100283, "start_date": "2017-02-01", "end_date": "2018-01-31", "raw_data": 170706}}]
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| Jan. 2, 2024, 4:16 p.m. |
Created
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[{"model": "core.projectfund", "pk": 53312, "fields": {"project": 15887, "organisation": 5, "amount": 100283, "start_date": "2017-02-01", "end_date": "2018-01-31", "raw_data": 142764}}]
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| Dec. 5, 2023, 4:25 p.m. |
Created
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[{"model": "core.projectfund", "pk": 46055, "fields": {"project": 15887, "organisation": 5, "amount": 100283, "start_date": "2017-02-01", "end_date": "2018-01-31", "raw_data": 125381}}]
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| Nov. 27, 2023, 2:16 p.m. |
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{"external_links": []}
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| Nov. 21, 2023, 4:44 p.m. |
Created
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[{"model": "core.projectfund", "pk": 38793, "fields": {"project": 15887, "organisation": 5, "amount": 100283, "start_date": "2017-02-01", "end_date": "2018-01-31", "raw_data": 81568}}]
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| Nov. 21, 2023, 4:44 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 117427, "fields": {"project": 15887, "organisation": 20055, "role": "PP_ORG"}}]
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| Nov. 21, 2023, 4:44 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 117426, "fields": {"project": 15887, "organisation": 20056, "role": "COLLAB_ORG"}}]
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| Nov. 21, 2023, 4:44 p.m. |
Created
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[{"model": "core.projectorganisation", "pk": 117425, "fields": {"project": 15887, "organisation": 11218, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:44 p.m. |
Created
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[{"model": "core.projectperson", "pk": 73908, "fields": {"project": 15887, "person": 21915, "role": "RESEARCH_COI_PER"}}]
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| Nov. 21, 2023, 4:44 p.m. |
Created
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[{"model": "core.projectperson", "pk": 73907, "fields": {"project": 15887, "person": 21916, "role": "COI_PER"}}]
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| Nov. 21, 2023, 4:44 p.m. |
Created
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[{"model": "core.projectperson", "pk": 73906, "fields": {"project": 15887, "person": 21917, "role": "PI_PER"}}]
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| Nov. 20, 2023, 2:06 p.m. |
Updated
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{"title": ["", "Database technology for deep marine clastic characterisation: upscaling for impact"], "description": ["", "\nThe geological characteristics of subsurface sedimentary rocks control the amount of oil, gas and/or water present within them (as hydrocarbon reservoirs or aquifers), and how such fluids will flow. Petroleum geologists build three-dimensional numerical models to assess the likely amount and flow rates of oil and gas and optimum well locations. These models ultimately determine whether hydrocarbon production is successful. (Hydrogeologists develop corresponding geological models to predict water yield or contaminant transport, in order to inform aquifer exploitation and clean-up; such models are also required to assess the feasibility of programmes of underground carbon capture and storage). When these models are built, geologists have available only limited direct subsurface data with which to constrain the type and geometry of subsurface geological bodies and thus their fluid-flow characteristics. To complement the sparse direct data, exposed outcrops of similar types of rocks, or modern sedimentary environments where comparable sediments are deposited, can be used as 'analogues' to hydrocarbon reservoirs or aquifers. These analogues provide proxy information regarding geological features that determine reservoir or aquifer heterogeneity. Within a reservoir or aquifer, these geological heterogeneities exert a primary control on well connectivity, flow rates, and behaviour to production or clean-up strategies, thereby dictating how much oil or gas is likely to be produced from a reservoir, or whether contaminants are successfully removed from the groundwater. Quantitative analogue data on these geological heterogeneities are required as input for constraining geological models of the subsurface. The derivation of this type of data from databases is an integral part of subsurface modelling workflows, but current approaches are inadequate because of the limited volume and quality of data stored in existing databases, and their current poor integration with existing modelling tools.\n\nThe Leeds IP consists of three different relational databases that contain analogue data about types of rock volumes that constitute the building blocks of geological models of reservoirs or aquifers; each database relates to a particular geological setting. All data are stored in a format that allows quantitative output to be produced, in forms that can be fed into all the common numerical methods used to build models of subsurface heterogeneity. The technology of the IP surpasses similar databases in terms of data quality and format. The fact that a fuller characterisation of sedimentary heterogeneity is achieved by these databases enables the derivation of the output required by existing modelling algorithms: this makes the IP unique in its class.\n\nHowever, the current value of the combined IP is limited by the relative underdevelopment of the Deep Marine Clastic database, and its current inability to integrate fully with software platforms employed to generate and manage geological models of the subsurface, such as Schlumberger's Petrel. Thus, the up-scaling of this database and the development of an interface for the optimal integration of the Deep Marine Clastic database with Petrel are key requirements for making this IP marketable, and leveraging the full value of the integrated databases. \n\nUpon successful development, the IP will enable easy access and application of large volumes of high-quality data in the area of Deep Marine Clastics, in parallel with that from other environments. This technology will aid geologists and engineers in the hydrocarbon and water-management industries in the generation of geologically sensible reservoir and aquifer models. \n\nThe project will be undertaken by Marco Patacci, currently a PDRA at Leeds, and supervised by Bill McCaffrey, who is a sedimentologist and director of the Turbidites Research Group, and Nigel Mountney (sedimentologist).\n\n"], "extra_text": ["", "\n\nPotential Impact:\nThe project has the potential to generate impact of economic, societal and academic nature.\n\nThe project execution will favour commercial uptake of the IP specific to the field of Deep Marine Clastics, and will further enable the value of a modelling approach that can be integrated across the full suite of clastic environments "from source to sink" i.e., across continental, shallow- and deep-marine settings, to be realised. Currently, the value of the IP is restricted by its unproven integration with industry workflows and the relatively small scale of the current database. Demonstration of technical feasibility for data integration into industry modelling workflows, and of the critical mass of data in the database will attract potential commercial partners. If successfully commercialised, the IP will have prompted a more widespread adoption of the associated technology in industry best-practice. It is foreseeable that this route will maximise the economic, societal and academic impact of the IP.\n\nIndustry uptake of the technology will result in the improvement of both the efficiency and end-results of the workflows applied to the subsurface geological modelling of hydrocarbon reservoirs and aquifers. These improvements will likely be reflected in the decision making for which these models are built. Crucially, better informed decision making regarding exploitation of natural resources has both societal and economic impact. The generation of more realistic subsurface models enabled by the technology will have an impact on the efficiency of hydrocarbon production, enhancing production volumes and rates and lowering costs. The high level of sophistication in the construction of subsurface geological models permitted by the IP is particularly required in application to hydrocarbon fields that are nearing depletion (e.g. many oil fields in the North Sea), and in the modelling effort required around the relatively immature use of such fields as CO2 sequestration reservoirs. The same IP would be applied in a similar way to create geological models of aquifers, which are routinely used to simulate groundwater yield and contaminant transport. The impact of the proposed IP on the modelling practice in the hydrogeology industry could be significant. Firstly because of a tendency to overlook geological realism when building aquifer models, in relation to the comparatively limited economic implications of the decisions taken in the water-management industry on the basis of these models, and secondly because of the potentially wider scale of the ultimate beneficiaries.\n\nCommercialisation of the IP will generate academic impact in a number of ways. Impact will be generated by demonstrating how fundamental-research outcomes are progressively translated into viable commercial applications; successful commercialisation will make this project serve as an impact case study. Popularization of the IP will promote engagement with academic researchers interested in the technology, widening possibilities for collaborative work, which would ultimately result in publishable output. Commercialisation of the IP will create opportunity for further collaboration and knowledge-transfer with industry, and will serve as an advertisement for support of academic work through industrial sponsorships.\n\n\n"], "status": ["", "Closed"]}
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| Nov. 20, 2023, 2:06 p.m. |
Added
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{"external_links": [62237]}
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| Nov. 20, 2023, 2:06 p.m. |
Created
35
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[{"model": "core.project", "pk": 15887, "fields": {"owner": null, "is_locked": false, "coped_id": "abd026c3-0f85-411a-ad97-a87ef1fdf2c9", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 81551, "created": "2023-11-20T14:00:06.528Z", "modified": "2023-11-20T14:00:06.528Z", "external_links": []}}]
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