Jose Chapela explores the challenges associated with data management and reviews solutions for overcoming them.

Data management has become a critical component of oil and gas exploration. The industry has spent years collecting, storing, analyzing and interpreting subsurface data. This data is invaluable for making informed decisions on where to explore for hydrocarbons, but properly managing subsurface data presents a multitude of challenges, each of which impacts on the quality, accessibility, and utility of this crucial resource. In this article, we explore the challenges associated with data management and review solutions for overcoming them.

Dealing with data in the upstream oil and gas sector has always presented distinct and complex hurdles. This industry handles a vast array of data, each marked by its own unique formatting intricacies. Consider file types like SEG-D, SEG-Y, ACSII, UKOOA, Multibeam, LAS, GeoTiff, and more, encompassing seismic data, well logs, horizons, interpretations, and various other data categories. Furthermore, a significant portion of these data formats include spatial components that demand meticulous handling to ensure accurate geolocation. This article will primarily address techniques for effectively managing your seismic data, whether it’s in SEG-Y, SEG-D, or even older formats like SEG-A, SEG-B, SEG-C, or SEG-X.

As you contemplate the next phase of data management, it’s crucial to acknowledge that the industry is currently shifting from its historical focus solely on hydrocarbons as the primary energy source to integrating renewables like wind and solar energy. This transition will bring entirely new challenges to our existing data storage and retrieval systems. If today’s data management already poses formidable challenges, one can only imagine the obstacles that tomorrow’s data managers will encounter. When considering the future of data management, here are the key questions you should ponder:

1. How can you devise a storage strategy that is both manageable and cost-effective, considering the exponential surge in data volumes, primarily seismic, witnessed over the past decade?
2. How can you amass sufficient metadata pertaining to your data, making it not only usable for your data management team but also for your processing, interpretation, exploration, machine learning, and artificial intelligence teams? Does this metadata support eventually align with the Open Subsurface Data Universe (OSDU) standards?
3. How do you facilitate the efficient transfer of data, not only within your internal teams but also with external partners and collaborators?
4. How can you organize and cleanse your data, gathered over decades, to ensure your machine learning and artificial intelligence initiatives are poised for success?
5. How can you design the next generation of adaptable data management solutions to effectively address these challenges while remaining flexible enough to accommodate future data sets? 

Read the full article here.