The environment in which pipelines must operate is changing rapidly and dramatically, as constant shifts in the global marketplace have underscored a need for increasing flexibility.

In this article, legacy 3D seismic data from the KG deep water basin underwent broadband reprocessing using state-of-the-art-software.

Carbon capture, utilization and storage projects are growing at record speed, resulting in an increasing need for subsurface technologies that can unlock fast time-to-results throughout all the steps of the project.

Digitalization in industrial facilities is critical to achieving new levels of safety, sustainability, and profitability—and AI is a key enabler for that transformation.

Discover how seismic volume-based unsupervised facies classification associated with advanced visualization and detection helps delineate the prospect’s potential, increase drilling success and reduce cost and risk.

Surface waves can generate coherent noise, known as ground roll, in seismic surveys. Ground roll can significantly degrade data quality.

This article shows how a machine-learning workflow based on a Self-Growing Neural Network (SGNN) was used by Aspen SeisEarth™ as an efficient and unbiased scanning tool for carbon capture and storage (CCS) monitoring, enabling faster identification of the confinement system.

Sustainability is emerging as a critical business topic, as companies focus resources toward lowering emissions, waste and energy use in their production process. In this article, Paige Morse, Chemicals Industry Marketing Director, shares how sustainability affects four key areas within a processing plant and how digital transformation supports sustainability efforts through efficiency improvements.

In recent decades, energy and chemical companies have benefited from digital technologies to increase the efficiency of hydrogen (H2) processes while ensuring safe and economical production, storage and transportation. Today, digitalization is increasingly crucial as hydrogen systems grow in complexity, utilizing green power sources and getting deployed at a larger scale.

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