Emerson has announced the availability of AspenTech Subsurface Intelligence (ASI), an open, cloud-native agentic environment that incorporates AI to transform the user experience and accelerate subsurface-related decision making while leveraging existing investments in legacy applications.
ASI’s AI-powered guidance capabilities and library of domain-specific agents operate with the industry-standard OSDU® Data Platform to automate workflows and develop insights that improve the speed from data to results and decisions. ASI fulfills a critical industry need to work in an agile, multi-disciplinary manner, optimize production and improve access to information trapped throughout various parts of the organisation.
Dr. Vikas Dhole, senior vice president of modeling and optimisation at Emerson’s Aspen Technology business, says: ‘Silos between disciplines, software and geographies are impacting the upstream industry’s ability to make fast, effective decisions about how to maximize returns for subsurface projects. ASI is transforming subsurface workflows by improving multi-disciplinary collaboration across the hydrocarbon extraction project lifecycle and accelerating investment decisions. Combined with decision support from an embedded AI advisor, ASI is enabling companies to more effectively navigate energy market volatility, overcome complex E&P challenges and empower a new generation of workers.’
ASI agents are strategically deployed as cloud-based applications with an intuitive user interface or with AI-powered guidance from Aspen Virtual Advisor. In this way, ASI enhances decision making across all skill levels, equipping the emerging subsurface workforce to confidently handle drilling, exploration and production challenges.
ASI’s open and scalable cloud-native environment fits into existing subsurface software ecosystems. ASI complements other products in Emerson’s AspenTech Subsurface Science & Engineering (SSE) portfolio and third-party technical applications, thereby allowing companies to reduce operating expenditures while bringing together individual disciplines and data to improve production time and leverage previous technology investments.
