Upstream companies today must achieve operational excellence by reducing emissions and utility demands, improving production at existing assets and replacing and expanding reserves while exhibiting capital discipline.

This paper presents a method for maximizing fault information from depth migrated narrow-azimuth as well as full-azimuth seismic data. The study demonstrates that depth domain diffraction imaging can be used to generate higher resolution fault definition than conventional reflectivity volumes, or their derivative post-stack attributes.

Full azimuth angle domain imaging provides an alternate way to map events in structurally complex areas. Information about continuous surfaces can be derived from specular gathers, while diffraction gathers are used to derive information about discontinuity i.e., faults and small-scale fractures.

We are excited to announce OPTIMIZE 24, our worldwide user conference, taking place April 29–May 3 in Houston, Texas.

Aspen SeisEarth is a one-stop shop for interpreters. This integrated solution suite provides fast, multi-survey structural and stratigraphic interpretation and visualization from regional to reservoir, enabling multiple users to collaborate in a single shared environment.

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 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.

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.

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

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.