Semiautomatic, Semantic Assistance to Manual Curation of Data in Smart Oil Fields

Vast volumes of data are continuously generated in smart oilfields from swarms of sensors. On one hand, increasing amounts of such data are stored in large data repositories and accessed over high-speed networks; On the other hand, 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, thus 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; since data is being 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 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.

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