Getting Legacy Data Online and Discoverable #welldata #digitize #seismic #bigdata #analytics #casestudy #machinelearning #metadata
21 May 19
4:00 PM
-
4:30 PM
Tracks:
Case Studies and Solutions
Systems and tools only provide as much value as the quality and quantity of data that is available in the system. As the Oil and Gas industry moves to cloud environments, and leverages data analytics and machine learning, one of the largest challenges facing the industry is getting legacy data online and discoverable. The focus of this presentation is to detail the successes and challenges of strategic initiatives to digitize hardcopy technical data, extract legacy seismic and well data from physical media and obsolete applications. Along with the data already online, this legacy data is foundational to a data centric digital environment of the future. ExxonMobil in 2017 initiated a large upstream wide project (which continues today) to scan, digitize, and capture enhanced metadata from ~12M archived hardcopy (paper) records. By leveraging service providers and local vendors, and employing the latest OCR and NLP techniques, the project provided visibility and value to previously difficult to find or unknown records. In addition, in 2018, ExxonMobil initiated a project to transcribe ~2-3M tapes containing seismic records currently stored in underground vaults. Again by leveraging local vendors for transcription and metadata capture, along with industry standard data models, increased value was obtained through faster access to better integrated data. Also in 2018, ExxonMobil started to develop workflows, and work with service providers to extract seismic, well, and interpretation data from currently obsolete or unsupported applications and archive tools. This effort posed significant challenges in determining business priority, and filtering derivative work products from final interpreted products. This effort also required multiple workflows for various legacy applications and archive tools. Legacy data capture and digitalization comes with significant effort and cost. These strategic initiatives have not only shown early wins, and added value to Upstream business units, but have enabled better data management, analytics, and machine learning opportunities.