Summary
Vast volumes of data are continuously generated in smart oilfields from
swarms of sensors. While increasing amounts of such data are stored in large
data repositories and accessed over high-speed networks, captured data is
further processed by different users in various analysis, prediction, and
domain-specific procedures that result in even larger volumes of derived
datasets.
The decision-making process in smart oilfields relies on accurate
historical, real-time, or predicted datasets. However, the difficulty in
searching for the right data mainly lies in the fact that data is stored in
large repositories carrying no metadata to describe them. The origin or context
in which the data was generated cannot be traced back, so any meaning
associated with the data is lost. Integrated views of data are required to make
important decisions efficiently and effectively, but are difficult to produce;
data generated and stored in the repository may have different formats and
schemata pertaining to different vendor products.
In this paper, we present an approach based on semantic web technologies
that enables automatic annotation of input data with missing metadata with
terms from a domain ontology, which constantly evolves while supervised by
domain experts.
We provide an intuitive user interface for annotation of datasets
originating from the seismic image-processing workflow. Our datasets contain
models and different versions of images obtained from such models, generated as
part of the oil exploration process in the oil industry. Our system is capable
of annotating models and images with missing metadata, preparing them for
integration by mapping such annotations. Our technique is abstract and may be
used to annotate any datasets with missing metadata, derived from original
datasets.
The broader significance of this work is in the context of knowledge
capturing, preservation, and management for smart oilfields. Specifically, our
work focuses on extracting domain knowledge into collaboratively curated
ontologies and using this information to assist domain experts in seamless data
integration.
© 2013. Society of Petroleum Engineers
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History
- Original manuscript received:
24 July 2012
- Meeting paper published:
19 March 2012
- Revised manuscript received:
7 December 2012
- Manuscript approved:
2 January 2013
- Published online:
22 January 2013
- Version of record:
1 February 2013
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