Fault detection and other time series opportunities in the petroleum industry

Authors:
Roar Nybø
Affiliations:
SINTEF Petroleum Research, Thormøhlensgate 53C, 5006 Bergen, Norway
Venue:
Neurocomputing
Year:
2010

Citing 3
Cited 1

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Abstract

Data-centric methods like soft computing and machine learning have gained greater interest and acceptance in the oil and gas industry in recent years. We give an overview of the opportunities and challenges facing applied time series prediction in this domain, with a focus on fault prediction. In particular, we argue that the physical processes and hierarchies of information flow in the industry strongly determine the choice of soft computing or machine learning methods.