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

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 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.

Sub-basalt seismic imaging in the Deccan traps, along the northwest coast of India, is very challenging, due to the morphology of the traps.

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.

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.

Predicting the permeability of fractured reservoirs is valuable for both reservoir assessment and drilling planning. Characterization of such systems requires advanced amplitude analysis, mainly based on seismic imaging results of the recorded wavefield.

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.

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.