By: Aveg Kadian, Associate Principal – Consulting, LTIMindtree

The Oil and Gas industry has been working with Petabytes of diverse data, of which raw, processed, and interpreted Seismic data are some of the most important datasets, especially for operators with active exploration assets. Currently, the industry is going through a strategic shift to accelerate and optimize Seismic data workflows. This technology shift will allow quick and seamless access to Seismic data from multiple applications, which will feed into integrated Seismic workflows, making the exploration team’s lives easier.

The main challenges with Big Data are categorized as the four S’s in Oil and Gas – Exploration:

Size: Scale of data – sheer data size/volume is the problem. E.g., 3D Seismic Data
Speed: Analysis around data streaming. E.g., Real-time Visualization
Selection: Variety of different forms of information/data forms. E.g., Subsurface Imaging
Sincerity: Veracity that revolves around the uncertainty of data. E.g., Reservoir Characterization.

A common challenge is the non-productive time caused by downloading, copying, exchanging, and conditioning Seismic data, making it difficult to transfer and use in workflows.

The question then is, Why Not Move Big Data (Seismic) to Cloud?

The next question that comes to our mind is if Cloud storage is that cheap, why isn’t everyone moving their seismic data to the Cloud? Currently, Seismic data is stored in different formats, both offline and online, across multiple locations. Major factors to consider are cost and scalability while moving subsurface data to Cloud. The most popular Cloud object storage can store massive data libraries (the choice is yours, depending on the need between Hot and Cold storage).

The Path Forward:

• Virtualize Workstation: Accessibility of data is significant to the overall performance of the application. So, if we move data away from the application, we may end up with more challenges. Fundamentally, the need is to virtualize the workstation by moving the application to the Cloud. Thus, we are assured that the application is virtualized in the Cloud, along with the Seismic data.
• Lift-and-Shift method: Now that the application and the data are in the Cloud, the application still doesn’t directly connect or have a way to interact with the object stores. The lift-and-shift method enables the movement of Seismic files to a file system in the virtual environment (moving a workload to the Cloud). But this may increase the cost of delivering the data requirements of the application.
• Compression Techniques: We need a sophisticated compression mechanism that prevents data loss and stores data in a Cloud-native format. This eliminates the need to move entire files to a virtual file system but send only the data needed in real-time.

The need here is to have a solution that begins with the Open Subsurface Data UniverseTM (OSDU) Platform compliant Seismic Data Lake with full resolution Seismic data hosted on a Cloud object storage. Seismic data will be stored in a way that will support a lossless and Cloud-native format. The solution will also allow flexibility in moving Seismic data to Cloud and ingestion to OSDU in both a SEG-Y and a compressed format. This will enable the solution to use a centralized Seismic platform to simplify using a standard format that would support Seismic data on the fly. This superior journey will allow O&G companies to adopt faster Cloud computing workflows, increasing efficiency and reducing time to decisions.