Seismic processing, imaging, characterization and interpretation are preferably executed as a continuous workflow to maintain seismic data integrity and consistencies. Geophysicists must construct a workflow from hundreds of applications and algorithms, and thousands of parameters, to achieve desired project outcomes. Almost all these applications and algorithms are based on assumptions about the underlying geological model complexity and subsurface conditions.

The past few years have seen increased interest in the application of machine learning in the industry, specifically to seismic interpretation.

There has been growing interest in the use of machine learning technologies for processing and interpreting seismic data. Many procedures that traditionally have been performed using deterministic methods and algorithms can be effectively replaced by neural networks and other artificial intelligence methodologies, improving simplicity, efficiency and automation.

The AspenTech DEI Team promotes an inclusive work environment that enables the success of all employees. Learn about the year ahead for DEI at AspenTech.

This case study details the work done at the Universiti Malaysia Pahang to research oleochemical fractionation to how to make the process more sustainable, reducing process carbon footprint. The case study explores the AspenTech solutions implemented as well as the value created.

This solution provides real-time, bi-directional connections, data visualization and increased efficiency for BASF's processes and customer value.

Read this case study to learn how ORYX GTL deployed Aspen InfoPlus.21 (IP.21) and AspenONE Process Explorer™ (A1PE)—as the underpinning of their digitalization journey.

Enhance real-time collaboration between internal and external stakeholders.

We welcome OSI and Geological Simulation Software businesses to AspenTech.

Pharma Manufacturing Enabling global access to medicines requires digital transformation