Cloud-based remote visualization of big data to subsurface exploration
Since the first visualization solutions were explored for O&G, major technical improvements enabled gigabyte-sized models to be rendered and manipulated at interactive speeds. Yet, other fundamental aspects such as data access and distribution are often overlooked in the process, even to date. Data movement may often be prohibitive, either due to legal constraints (data is restricted from departing the country) or practical considerations (data is too large to be moved, is a checkpoint tightly connect to imaging processes, and requires costly resources to be manipulated). Collaborative visualization tends to be performed co-locally, following an explicit, manually conducted, data transfer to a dedicated visualization machine. We propose an alternative based upon data-centric computing. The model offers visualization as-a-service over a multi-tenant cloud-based environment. Remote visualization enables lightweight access and interaction with generated data readily after the processing, dismissing the need to transfer the whole dataset into analyst's machine. It offloads heavy graphics processing to a cloud server featuring the necessary infrastructure to handle such voluminous data, like GPUs, GPFS, and ultimately sends only a reduced output to lightweight clients (rendered images/geometry). Visualization resources can also be shared among concurrent users in a web-based interface and combined with other data sources, like correspondent well information or velocity models, facilitating effective remote collaboration towards knowledge discovery in subsurface exploration. Ubiquitous on-the-go data access (e.g., in the exploration field itself) is thereby made possible through mobile interfaces. Concurrently, several challenges emerge with the aforementioned visualization model. The effective resource distribution of different data sources among several clients needs to benefit from the cloud execution platform. The OpenPower Foundation is an example of the future HPC platform that can be customized to O&G characteristics and be offered as a cloud model.