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.

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.

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.

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.

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.

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

In the upstream oil and gas industry, multiphase metering is a central part of many processes, such as reservoir management, field development, operational control, flow assurance and production allocation. Industrial Internet of Things (IIoT) systems unlock online production monitoring and allow field operators to monitor their wells continuously and in real-time.

Pore pressure prediction and poroelastic modeling help understand the mechanical properties of the subsurface and are regularly used in industries such as oil and gas, geothermal, and CO2 storage.

The rapid and nonintrusive deployment of seismic sensors for near-surface geophysical surveys can make data acquisition more efficient, in a wide variety of environmental and surface-terrain conditions.

Fluvial depositional environments play a major role in hydrocarbon reservoirs and have therefore received considerable attention in the domain of reservoir modeling.