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.
Full-Azimuth Differential Seismic Facies Analysis for Predicting Oil-Saturated Fractured Reservoirs
This work presents a novel technology for azimuth-dependent facies analysis (Facies Analysis versus Azimuth – FACIVAZ) to improve the prediction of hydrocarbon-saturated permeable fractures in terrigenous carbonate reservoirs. The analysis is performed in the depth domain along high-resolution, full-azimuth, angle domain common image gathers created by the Aspen EarthStudy 360™ imaging system.
CRAM Gathers Enhance 3-D Inversion
Elastic inversion from common reflection angle migration (CRAM) gathers can accurately capture lithology-driven lateral variations in reservoir properties, particularly in a strongly deformed and faulted geologic environment.
Improved Seismic Images through Full-Azimuth Depth Migration
A seismic survey was conducted in a production oil field in Serbia. It was assumed that significant reserves still exist in the field, as well as additional undiscovered reservoirs. An advanced seismic imaging technology was required to further characterize the existing reservoirs and identify and characterize new ones.
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