Exploration and Production (E&P) companies realize the importance of good technical data and the role which it plays in the exploration success. Geoscientists and engineers focus on maximizing hydrocarbon extraction by understanding and exploiting field geological and formations data. To achieve their challenging goals, they use sophisticated software to perform virtual field characterizations and analysis which generates terabytes of valuable intellectual data and information. This is one example, and there are many more by which new data is being generated and stored each day. Companies are investing into hardware and software to tackle these problems but lack the basic guidelines or policies to manage the growing data volume. This has led to even more problems and waste of valuable resources and funds. One of the key requirements for E&P companies is to have a well-defined data management strategy.
Classroom or Online
- Upstream personnel – understanding the life cycle.
- Data Management SME’s that want to understand Geophysical Data.
- IT – Technology Change in an industry with legacy systems.
- Admin – Managing end to end workflow streams from acquisition to interpretation.
- HR – Understanding the unique blend of geophysical data, I.T. and Data Management.
- Non-geoscience personnel – Understanding the scope.
- Geophysical Data Management life cycle, why it is important, understanding Data Acquisitions in terms of exploration, production, and life of the field.
- Core Geophysical data types in the E&P industry and valuable best practices for them.
- Common seismic activity using Gulf of Mexico as a case study.
- Big Data, the cloud, managing Petabytes of data, key factors, structure, workflow, access, timing.
- Business Intelligence maturity modelling in the Oil and Gas industry.
- Limitations and challenges with Geophysical Data – specific esoteric nature of managing signal binary data types.
- Being familiar with industry data standards.
- Global Opportunities – Market Size – Players.
- Technology Challenge – Data Analytics and AI.