Research On The Key Technologies Of Domain Data Integration And Service

Data is the source of scientific and technological innovation, and domain data integration and sharing are the foundation of data-intensive application. However, data sources are usually characterized as distributed and heterogeneous systems, thus it is hard for them to interact mutually. Moreover, semantic inconsistency has become an even greater problem on the information or knowledge sharing among users or applications. Therefore, domain-specific semantic integration is a research hotspot. Due to the different features in different domain, data integration always falls in a specific domain. This thesis explores the research on how to apply data integration technology to the oil & gas lifting engineering domain, and discusses some key technologies such as the domain public data model and semantic integration, and presents an application service system base on the domain data cloud. This paper aims to combine the distributed and heterogeneous data resource with domain applications to implement data integration and sharing. The main work and contributions are summarized as follows:(1) We propose an implementation framework for oil & gas lifting engineering data cloud and present an architecture for oil & gas lifting engineering data application service by integrating the domain practical requirements and domain data situation of CNPC (China National Petroleum Corporation). With the help of this hierarchical architecture, the integration of distributed heterogeneous data sources is achieved, which provides convenient data support for the sharing of the production engineering data and the development of data services.(2) A domain public data model DMO (Data Model for Oil & Gas Lifting Engineering Data) is designed to provide a formal representation method for oil & gas lifting engineering data and knowledge. According to the need of oil & gas lifting engineering domain, the model provides a method to represent oil & gas lifting engineering data and knowledge formally. The model proposes a largest public substring algorithm to extract data entity, and the internal relationships are described by Description Logic axioms and rules which can be used to deduce explicit knowledge.(3) To solve the semantic heterogeneity problem in production engineering data, a semantic integration method of production engineering data based on domain-specific ontology is proposed. By developing domain-specific ontologies and semantic models, and building mapping relationships between ontologies and corresponding elements, a unified semantic association upon different data sources is realized. A semantic query and integration of domain-specific data are implemented. This semantic integration method is applied to real systems such as oil & gas assist decision, optimization design and condition diagnosis system, and the effectiveness of this method is proved.(4) Based on the requirement of oil and gas lifting engineering domain, we build the service application system. The system has been applied for eleven months in four oilfields including Daqing oilfield, Dagang oilfield, Huabei oilfield and Jilin oilfield. We have already loaded the data of 30817 oil & gas wells of 4 oilfields. The system bring excellent effects in practice.