首页>
外文会议>Intelligent Energy International Conference
>A Unified Framework for Implementing Business Intelligence, Real-time Operational Intelligence and Big Data Analytics for Upstream Oil Industry Operators
【24h】
A Unified Framework for Implementing Business Intelligence, Real-time Operational Intelligence and Big Data Analytics for Upstream Oil Industry Operators
For the last few years, ADCO has been implementing a program for business intelligence and data management. With the increasing demand for higher production capacity and with the recent evolutions in information, communication and operational technology, ADCO realized the importance of evolving the scope of the program in order to take into consideration real-time operational intelligence and big data analytics initiatives and get prepared to adequately support them. Accordingly, ADCO has established a unified framework for the new program that aims to deliver standardized and integrated solutions for all of the above initiatives. This framework is based on a set of best practices, proven enterprise architecture methodologies, industry standards and reference models. This paper describes how ADCO has selected, combined and tailored the elements of the above set to form a unified framework that suits the overall new program objectives. It also presents the current and future state architectures, and-sample-use-cases-and-applications'-outputs. The above framework can be applied by business and IT architects, strategists and program managers to coordinate and optimize their relevant enterprise initiatives. By early establishing the above framework, ADCO gained more insight regarding the requirements of future phases. Instead of only relying on out-of-the-box operational intelligence applications, ADCO has also considered additional approaches such as self-services analytics to complement them. ADCO identified the need to modify its technology acquisition plans to include new category of tools and equipment. The need for adapting the de-facto standard development methodologies was identified and the framework was tailored to address it. This framework was also tailored to efficiently support big data-requirements. Using the above framework realizes better standardization, integration, architecture simplification,-data-consistency-and-cost-optimization. Although there are different frameworks that may be used individually to support the above initiatives, the presented framework is a unified way to support and coordinate all of them together.
展开▼