Exploration and production (E&P) organizations have goals to find more oil and gas reserves by increasing existing assets production and finding new assets through exploration. With the decreasing number of experts to develop and manage increasingly complex assets, new ways to operate have to be found. Workflows that allow cross-domain collaboration and real-time operations are being developed, taking advantage of powerful applications and information management best practices. As these workflows are deployed, asset groups realize that current information technology (IT) infrastructures need to be transformed to enable changes.

Defining how to transform the IT infrastructure requires understanding of the exploration, development and production domains, as well as IT expertise. The asset organization information management landscape and the underlying IT infrastructure need to be assessed in light of workflow requirements. Before starting infrastructure transformations, a vision of the future IT architecture is defined based on the organization objectives and processes. Alignment with corporate IT standards is facilitated when domain experts who also understand IT can translate E&P domain needs into IT infrastructure requirements.

Through assessments and architecture definitions performed with E&P customers we can give examples of complex IT infrastructure transformation projects that combine many IT domains such as applications, data management, security, connectivity, systems, servers, storage, and disaster recovery. Emerging IT technologies such as SOA (Services Oriented Architecture), wireless connectivity, virtualization, and high-performance computing can be leveraged and their deployment carefully planned to deliver the expected benefits. Using an IT infrastructure roadmap to guide projects will result in an IT infrastructure that works seamlessly and delivers performance, ease of use, and remote and secure data access to the asset teams.

Designing a global IT vision that leverages E&P workflows, applications understanding, and data management knowledge is an efficient methodology to gain efficiency and value from workflow optimization efforts.

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