| EPSRC Reference: |
GR/L10031/01 |
| Title: |
BAYES LINEAR FORECASTING AND DECISION MAKING FOR LARGE SCALE PHYSICAL SYSTEMS IN THE PETROLEUMINDUSTRY |
| Principal Investigator: |
Goldstein, Professor M |
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| Department: |
Mathematical Sciences |
| Organisation: |
Durham, University of |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 January 1997 |
Ends: |
31 March 2000 |
Value (£): |
143,598
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| EPSRC Research Topic Classifications: |
| Statistics & Appl. Probability |
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Summary on Grant Application Form |
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In the petroleum industry, much forecasting and decision making is directed by models of large physical systems. As such models are slow to evaluate, very high dimensional, and values of inputs and predictive reliability of outputs are uncertain, we must elicit and exploit expert knowledge. However, the usual Bayes approach is infeasible due to real time constraints. On a previous collaborative EPSRC contract, with SSI, we developed a Bayes linear strategy for history matching (identifying input values so that models reproduce historical production), combining elicitation tools with analysis of simplified versions of the models. This approach suggests a practicable methodology: the simplified models are sufficiently fast that we may account for model uncertainties using repeat runs of these models to produce prior judgements as to forecast outcomes and good decisions, which are updated by selected full model evaluations. We shall develop prior descriptions relating uncertainties in the models and the physical system, and Bayes linear methodology for forecasting and decision making using these descriptions. We will test the methodology on case studies provided by SSI concerning reservoir management and real time decisions such as leak detection for pipeline networks.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
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| Description |
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Sectors submitted by the Researcher |
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