Recently,many heavy-oil fields have seen exponentially higher volumes of data made available as a result of omnipresent connectivity.Existing data platforms have focused traditionally on solving the problem of data storage and access.The more-complex problem of true knowledge discovery and systematic value creation from the massive amount of data is less frequently addressed.The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields.Optimal reservoir management for heavy-oil reservoirs requires systematic solutions that combine both engineering ability and advanced analytics.The authors believe that this requirement is addressed by what they call augmented artificial intelligence(AAI),a process inspired by the intelligence-amplification concept in which machine learning and human expertise are combined to improve solutions derived by systems that learn without any type of input from engineers or geoscientists.Practical deployment of AAI will involve automated work flows that use solid technical expertise and proven processes to transform field data into more-effective reservoir-management solutions.
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