Common in the energy sector, satellite images, geospatial data, and seismic surveys are used to increase the effectiveness of drilling operations. These surveys can consume PBs of data and are constantly growing as companies continue exploration for new oil-rich reserves.
Leveraging AI tools across multiple clouds to analyze this dataset can radically improve the chances of finding these reserves, short-circuiting lengthy timelines. Leverage industry-standard protocols including NFS, SMB, HTTP, FTP and Hadoop HDFS to provide an efficient and flexible shared petrotechnical storage infrastructure – a scale-out data lake – that can support a wide set of applications and analytics methods.
Leverage best of breed tools across all cloud providers to accelerate the identification of oil-rich reserves, reducing exploration costs and driving revenue growth.
REDUCED COMPLEXITY WITH CENTRAL DATA LAKES
AI and machine learning require large amounts of “training data” to learn and recognize patterns using the centrally stored geospatial and survey images.
FLEXIBLE TOOLS FOR DIVERSE TECHNOLOGY TEAMS
Existing teams can use their cloud-based Analytics and AI tools of choice across all the cloud providers, speeding up time to value without requiring new skills and knowledge.