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.

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) data historian and AspenONE Process Explorer™ (A1PE)—as the underpinning of their digitalization journey.

Pharma Manufacturing Enabling global access to medicines requires digital transformation

In a mature onshore North American basin with a wealth of information, an operator needed to streamline data integration and interpretation in order to enable efficient prospecting. Read how Aspen SKUA™ software was used to develop multiple custom-made macros to automate repetitive and time-consuming tasks, and to create a new mapping workflow that enabled the customer to efficiently create prospecting maps to assist in the strategic acquisition of oil and gas leases.

As a leading synthetic fuel plant based in Qatar, ORYX GTL’s mission is to “add value to Qatar’s natural resources by applying innovative technology.” Reducing CO2 emissions and flaring at the world’s first-ever, commercial-scale Gas-to-Liquid (GTL) plant is key to supporting this mission. More specifically, the company is using advanced process analytics to achieve its ambitious operational goals.

A global petrochemical producer wanted to improve monitoring and efficiency, specifically starting with energy consumption, of its pyrolysis furnaces by integrating a predictive vision. With the current process, it was challenging to maintain unit efficiency and stay at a consistent operating level. The company was able to solve these challenges by implementing Aspen ProMV™.