Operational excellence, digitalization and sustainability were all key topics of discussion at the 27th Annual ARC Forum. AspenTech CMO, Lawrence Schwartz details his experience.

Novozymes A/S, a global biotechnology company based in Denmark, was looking to support the development and optimization of biodiesel processes due to increasing biodiesel market demand, rising materials costs and more stringent industry regulations. Novozymes saw potential in combining AspenTech technology with its own unique design and processing expertise to more quickly identify new opportunities to increase profitability of biodiesel production processes.

At the annual ARC Forum meeting in Orlando, we highlight the dual challenge of meeting the growing demand for resources while addressing sustainability goals.

Achieve supply chain resilience with downtime scheduling that leverages prescriptive maintenance and advanced scheduling optimization to minimize impact on production.

A global leader in industrial data management from the shopfloor to the boardroom, we accelerate data-driven value creation in asset-intensive industries through robust data software offerings. 

To celebrate Black History Month, Byron Gautier, a member of the AspenTech Black Leadership Forum, explores how African culture has shaped our food, drink and American popular culture.

AspenTech DataWorks is purpose-built to be the global leader in industrial data management.

Seismic data can provide useful information for prospect identification and reservoir characterization. Combining seismic attributes helps identify different patterns, for improved geological characterization. Machine learning applied to seismic interpretation is very useful in assisting with data classification limitations.

In depth imaging, depth calibration using well-tie updated velocity is generally applied to poststack data. This is the most correct type of depth calibration as it positions structures in their proper places.

One of the main challenges in seismic imaging, especially of land data, is building the near surface velocity model, as it is normally characterized by very low velocity values with different types of local anomalies. Resolving the near surface velocity model using only refraction data is insufficient, as it does not provide the required lateral resolution. Using only reflection data is equally insufficient, as it does not provide the required vertical resolution.