White Paper

Near-Field Exploration and Development: A Holistic Look at Leveraging Digital Technologies to Increase Productivity and Profitability

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.

Technical Paper

Efficacy of Diffraction Imaging for Identification of Faults and Fractures: A Case Study with (a) Full Azimuth 3D Land Data and (b) Narrow Azimuth 3D Marine Data

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.

Technical Paper

Improved Imaging and Subtle Faults and Fracture Characterization using Full Azimuth Angle Domain Imaging: A Case Study from Cambay Basin, India

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.

User Conference


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


Aspen SeisEarth™

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.


Synthetic Seismic Data Generation for Automated AI-Based Procedures with an Example Application to High-Resolution 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.


Using a Self-growing Neural Network Approach to CCS Monitoring

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.


Seismic AVO Attributes and Machine Learning Techniques Characterize a Distributed Carbonate Build-Up Deposit 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.


Comparing Bayesian and Neural Network Supported Lithotype Prediction from Seismic Data

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


Characterizing Seismic Facies in a Carbonate Reservoir Using Machine Learning Offshore Brazil

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.

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