COURSE OVERVIEW:

The modern interpretation of 3D seismic data relies heavily on the extraction of attributes to illuminate subtle geological features that are often invisible on standard amplitude sections. This course provides an advanced exploration of the mathematical and physical basis of seismic attributes, focusing on their application in reservoir delineation and property prediction. Participants will examine the full spectrum of attributes, from basic geometric measurements to complex multi-spectral and physics-based transformations.

The scope of this training covers the strategic selection and combination of attributes to solve specific subsurface challenges, such as identifying fractured corridors, mapping thin-bed sands, or detecting fluid anomalies. Attendees will learn the distinction between physical attributes, which relate to rock and fluid properties, and geometric attributes, which emphasize structural discontinuities. We will also investigate the preprocessing requirements for attribute calculation to ensure that noise does not bias the final characterization.

Coverage includes the integration of attributes through neural networks and machine learning for facies classification and property upscaling. By utilizing advanced visualization techniques like RGB blending and multi-attribute overlays, geoscientists can create highly detailed maps of reservoir heterogeneity. This course empowers professionals to move beyond qualitative interpretation into quantitative reservoir characterization using the full power of 3D seismic volumes.

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

• Define the physical and mathematical foundations of various seismic attributes.
• Select appropriate attributes for specific geological and reservoir objectives.
• Apply geometric attributes like Coherence and Variance to map fault networks.
• Utilize Curvature attributes to identify small-scale fractures and folds.
• Analyze Spectral Decomposition outputs to resolve thin-bed reservoir thickness.
• Use Instantaneous Attributes (Phase, Frequency, Amplitude) to detect anomalies.
• Perform RGB blending for enhanced visualization of depositional systems.
• Evaluate seismic textures using GLCM (Grey Level Co-occurrence Matrix) methods.
• Link seismic attributes to reservoir properties using rock physics models.
• Apply multi-attribute neural network analysis for facies classification.
• Differentiate between geological signal and acquisition/processing footprints.
• Design workflows for noise attenuation before attribute computation.
• Integrate attribute-derived maps into the 3D static geological model.

TARGET AUDIENCE:

Geophysicists, Interpretation Specialists, Reservoir Geologists, and Asset Team Leads focused on quantitative reservoir description.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximise the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.