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The Computational Optimization Group (COG) in the Department of Computing produced new models, algorithms, and approximations for supporting confident decision-making under uncertainty — when computational alternatives are scarce or unavailable. The impact of this research is exemplified by the following:
ERPE research into uncertainty quantification for oil reservoir modelling, described in this case study, has led to 3 impacts in the REF2014 period:
Many organisations rely on increasingly large and complex datasets to inform operational decision- making. To assist decision-makers when decisions are data-driven, computational tools are needed that present reliable summary information and suggest options allied to the key objectives of decision-making. Research at RGU has developed novel learning and optimisation algorithms driven by multifactorial data and implemented this in commercial decision-support software. The research has had economic impact by providing products to be sold: drilling rig selection tool (ODS-Petrodata Ltd.) and subsea hydraulics diagnostic tool (Viper Subsea Ltd.). Further economic impact comes from operations management software developed for British Telecom.
Research by Gondzio (Maxwell Institute) on algorithms for large-scale optimization has led to major advances in the design of interior point methods (IPMs). The advances include new ways of exploiting centrality (1996-2008) as well as special preconditioning (2004) and warmstarting (2003, 2008) techniques. These techniques make it possible to solve more difficult optimization problems more quickly. Some of these have been implemented by all major commercial providers of optimization software including IBM, Gurobi, Mosek and FICO. The techniques have therefore had an economic impact on these companies and on thousands of their customers worldwide who now benefit from faster, more reliable methods to solve their challenging optimization tasks.
Professor Wright has developed practical scheduling implementations for sports fixtures and officials, with regular clients at both professional and amateur level in the UK and abroad, including the England and Wales Cricket Board and the New Zealand Rugby Union. His expertise also supports `what if' exercises, enabling clients to experiment with new ideas and announce changes with confidence that they will work in practice. His work has resulted in financial gains, substantial savings in skilled administrative time and high satisfaction for stakeholders. His research has potential reach across numerous sports, at all levels across the world.
Since Prof Blunt's appointment as a Professor of Petroleum Engineering at Imperial College in 1999, his Consortium on Pore-Scale Modelling has developed numerical tools to analyse the pore spaces of reservoir rocks, predict multiphase flow properties and determine field-scale impacts on oil recovery. This technology is now exploited by at least two start-up service companies with annual revenue of around $20 million, and is widely employed by major oil companies, leading to better reservoir management and improved oil and gas recovery. Statements submitted from just one company (Kuwait Oil Company, KOC) suggest a benefit of $100 million from efficiency savings and improved recovery in a just single field.
Research by the University of Aberdeen's research group on Stratigraphic Evolution of large Igneous Provinces (StratLIP) has guided the successful development of new oil-producing fields in the North East Atlantic that were previously not in production, aided by an improved understanding of the geological context within which the reserves were discovered. The research has informed every phase of exploration and development by several of the UK's leading energy companies, in one project saving the partners £600m and proving the financial viability of a major oilfield development deemed important to the UK's oil supply. The findings have contributed to an increase in the UK's energy security and the strength of the UK's oil and gas industry, especially in the context of the local economy of Aberdeen, the energy capital of Europe.
One of the major problems experienced in the oil production industry is the formation of mineral scale deposited downhole within an oil reservoir and topside. The scale creates a blockage causing a detrimental effect to the productivity of the well. ERPE Research in scale management has led to the following impacts in the REF2014 period:
ENABLE is a history matching and uncertainty assessment software system for the oil industry, whose inference engine was produced by the Durham Statistics group, based on their research on uncertainty quantification for complex physical systems modelled by computer simulators. The system optimizes asset management plans by careful uncertainty quantification and reduces development costs by accelerating the history matching process for oil reservoirs, resulting in more informed technical and economic decision-making. ENABLE was acquired by Roxar ASA in 2006 and current users include the multinational oil company Statoil. From January 2008 to September 2012 (the most recent set of figures) the turnover attributed to ENABLE was [text removed for publication].
Payment card fraud is a significant cost to business, as well as being a route to funding of organised crime, drug smuggling and terrorism. Detection of fraud requires a technique that is both transparent and adaptive. We have used the Department of Computing's expertise in machine learning and rule induction to develop a scalable method of automated fraud detection that meets the industry's needs. This technique is now being commercialised by AI Corporation, with a contract for its use having been placed by the world's largest retailer. Contracts with major banks are currently under negotiation.