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Reference Number  EP/K034154/1  
Title  Enabling Quantification of Uncertainty for LargeScale Inverse Problems (EQUIP)  
Status  Completed  
Energy Categories  FOSSIL FUELS: OIL, GAS and COAL(Oil and Gas, Enhanced oil and gas production) 75%; NOT ENERGY RELATED 25%; 

Research Types  Basic and strategic applied research 100%  
Science and Technology Fields  PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 60%; ENVIRONMENTAL SCIENCES (Earth Systems and Environmental Sciences) 40%; 

UKERC Cross Cutting Characterisation  Not Crosscutting 100%  
Principal Investigator 
Professor A Stuart No email address given Mathematics University of Warwick 

Award Type  Standard  
Funding Source  EPSRC  
Start Date  01 June 2013  
End Date  30 November 2018  
Duration  66 months  
Total Grant Value  £2,048,925  
Industrial Sectors  Construction  
Region  West Midlands  
Programme  NC : Engineering, NC : Maths  
Investigators  Principal Investigator  Professor A Stuart , Mathematics, University of Warwick (99.997%) 
Other Investigator  Professor M Christie , Institute Of Petroleum Engineering, HeriotWatt University (0.001%) Professor G O Roberts , Statistics, University of Warwick (0.001%) Professor M Girolami , Statistical Science, University College London (0.001%) 

Industrial Collaborator  Project Contact , AWE Plc (0.000%) Project Contact , BG Group (0.000%) Project Contact , Rock Flow Dynamics (RFD), USA (0.000%) 

Web Site  
Objectives  
Abstract  A mathematical model for a physical experiment is a set of equations which relate inputs to outputs. Inputs represent physical variables which can be adjusted before the experiment takes place; outputs represent quantities which can be measured as a result of the experiment.The forward problem refers to using the mathematical model to predict the output of an experiment from a given input. The inverse problem refers to using the mathematical model to make inferences about input(s) to the mathematical model which would result in a given measured output.An example concerns a mathematical model for oil reservoir simulation. An important input to the model is the permeability of the subsurface rock. A natural output would be measurements of oil and/or water flow out of production wells. Since the subsurface is not directly observable, the problem of inferring its properties from measurements at production wells is particularly important. Accurate inference enables decisions to be made about the economic viability of drilling a well, and about wellplacement.In many inverse problems the measured data is subject to noise, and the mathematical model may be imperfect. It is then very important to quantify the uncertainty inherent in any inferences made as part of the solution to the inverse problem. The work brings together a team of mathematical scientists, with expertise in applied mathematics, computer science and statistics, together with engineering applications, to develop new methods for solving inverse problems, including the quantification of uncertainty. The work will be driven by applications in the determination of subsurface properties, but will have application to a range of problems in the biological, physical and social sciences  
Publications  (none) 

Final Report  (none) 

Added to Database  14/08/13 