The future of the upstream industry lies in the adoption of innovative geoscience digitalization solutions to optimize operations. Machine learning applications for geoscience data have been in use for more than 25 years but have recently become critical due to massive growth in the amount of petrotechnical data being acquired. As machine learning evolves, it will play an increasingly visible role in analyzing surface and subsurface data.

With mounting pressure to reduce carbon emissions—and still meet critical production targets—today’s energy and chemical companies are challenged to stay on top of changing operating conditions and respond in a timely, efficient and coordinated manner.

For decades, first principle models have been used for process design and optimization in the chemical industry. Recent innovations employ machine learning techniques—enriched with first principle constraints—to create a hybrid model that closely represents real plant behavior. Fast, easy access to industrial AI is helping organizations make accurate decisions to select an optimum design quickly and improve operations, maximizing productivity and minimizing energy usage.

With more focus on sustainability, frequent changes in global gas markets and evolving processing technologies, gas plants are always looking to improve operations by accurately simulating and predicting increasingly complex processes.

See how AspenTech DataWorks is improving our best-in-class data management solutions with new capabilities and synergies.

Learn how to migrate your industrial data from a variety of sources to the AWS cloud in this on-demand webinar featuring experts from ARC, AWS and AspenTech.

Industrial data from a variety of sources and locations has the potential to help organizations meet energy efficiency goals, avoid raw materials/feedstock shortages, decrease downtime, increase profitability and improve ESG reporting. But the chemical industry, like other asset-intensive industries, generates large amounts of data that never gets collected, contextualized and used to its full potential.

The successful execution of engineering projects requires intense collaboration among multi-discipline teams, but manual data handoffs often lead to errors, inconsistencies and delays that can derail a project.

To chemical and polymer manufacturers, achieving site optimization and resiliency to disruptions is key to meeting the world's ever-changing demand. Take a deep dive look into the flywheel effect of how Advanced Process Control (APC) and Scheduling synergies takes manufacturing sites to another level of performance and resilience. Leverage both of these crucial layers together to multiply the benefits of each other.

Distributed Energy Resource Management Systems (DERMS) are quickly gaining adoption as an indispensable tool for managing the large influx of distributed energy resources (DERs). The ability to optimize and orchestrate huge numbers of DERs is essential to integrate large amounts of intermittent renewable generation while also ensuring grid reliability.