Course Objectives:

By the end of this training, participants will be able to:

1. Introduction to Seismic Data and Quantitative Interpretation

• Understand the fundamentals of seismic data acquisition and its role in subsurface exploration.
• Learn the principles of quantitative interpretation (QI), where seismic data is used to estimate physical properties of the subsurface, such as porosity, permeability, lithology, and fluid saturation.
• Gain an understanding of the difference between qualitative interpretation, which focuses on visual analysis, and quantitative interpretation, which involves numeric modeling and data integration.

2. Overview of Seismic Data Types and Properties

• Explore different types of seismic data, such as 2D seismic, 3D seismic, and 4D seismic (time-lapse seismic data), and understand the unique characteristics of each.
• Understand key seismic attributes like amplitude, velocity, frequency, phase, and their role in the interpretation process.
• Study the relationship between seismic data and reservoir properties, including how seismic data is linked to rock physics.

3. Seismic Inversion Techniques

• Understand the concept of seismic inversion, which involves converting seismic data into quantitative subsurface models, including impedance and velocity models.
• Learn about the different types of inversion techniques, such as:
• Post-stack inversion
• Pre-stack inversion
• Avo inversion (Amplitude Versus Offset)
• Study the use of acoustic impedance and elastic impedance in seismic interpretation and how they help determine lithology and fluid content.
• Gain practical knowledge in using inversion methods to generate synthetic seismograms and improve depth conversion.

4. Rock Physics and Seismic Attribute Analysis

• Learn about rock physics models that relate seismic attributes to the physical properties of the reservoir, such as bulk modulus, Poisson’s ratio, and Young’s modulus.
• Study how rock physics can help interpret seismic attributes like amplitude variations, AVO responses, and seismic velocity for subsurface analysis.
• Understand the integration of seismic data with well logs and core data to perform rock physics analysis for better prediction of subsurface properties.
• Learn about seismic-to-property workflows, where seismic attributes are calibrated with actual subsurface data.

5. Seismic AVO (Amplitude Versus Offset) Analysis

• Explore the concept of AVO analysis and how it is used to estimate reservoir properties such as porosity, fluid type, and lithology.
• Understand how AVO analysis can differentiate between gas, oil, and water-filled reservoirs based on seismic responses to varying offsets.
• Learn to use AVO crossplotting to interpret seismic data and correlate amplitude changes with fluid saturation and lithological variations.

6. Seismic Attribute Extraction and Interpretation

• Study various seismic attributes and how they can be extracted from raw seismic data to help in reservoir interpretation, such as:
• Amplitude
• Frequency
• Phase
• Continuity
• Coherence
• Understand the application of time-domain and frequency-domain seismic attributes in detecting faults, fractures, and layering in the subsurface.
• Learn how to perform seismic attribute analysis using software tools to generate maps, crossplots, and other visualizations for better reservoir understanding.

7. Quantitative Interpretation with Seismic-to-Well Integration

• Learn about the integration of seismic data with well log data to improve the accuracy of reservoir models.
• Understand the process of seismic-to-well tie (synthetic seismograms) and how it is used to correlate seismic amplitudes with physical properties obtained from well logs.
• Study how seismic inversion and well-to-seismic calibration improve the accuracy of subsurface predictions, such as porosity and fluid content.

8. Seismic Time-to-Depth Conversion

• Study the principles of time-to-depth conversion, where seismic data (usually in time domain) is converted to depth domain for better spatial interpretation.
• Understand how velocity models are used in the conversion process, and the importance of accurate velocity analysis for reliable depth conversion.
• Learn about depth imaging techniques such as time migration and depth migration and their role in improving the accuracy of subsurface structural interpretation.

9. Multi-Attribute Analysis and Machine Learning in QI

• Explore the concept of multi-attribute analysis, where multiple seismic attributes are combined to create predictive models of subsurface properties.
• Study the role of machine learning and artificial intelligence in improving quantitative interpretation, including:
• Automated pattern recognition
• Neural networks for property prediction
• Data-driven models for reservoir characterization
• Learn how machine learning algorithms can enhance the accuracy of seismic inversion, well-to-seismic calibration, and attribute analysis.

10. Case Studies: Quantitative Interpretation Applications

• Study real-world case studies where quantitative seismic interpretation has been successfully applied in different geological settings, including:
• Oil and gas exploration
• Carbonate reservoirs
• Shale gas reservoirs
• Deepwater exploration
• Analyze how seismic inversion, AVO analysis, and rock physics workflows were used to make key decisions in reservoir modeling, drilling, and production optimization.

11. Software Tools for Quantitative Seismic Interpretation

• Learn about the key software tools used in seismic data processing and quantitative interpretation, such as:
• Petrel
• Hampson-Russell
• GeoTeric
• Oasis montaj
• Gain hands-on experience with seismic interpretation software to extract seismic attributes, perform inversion, and integrate with well data for improved reservoir characterization.

12. Challenges and Future of Quantitative Interpretation

• Understand the challenges faced during quantitative seismic interpretation, such as data quality issues, resolution limitations, and geological uncertainty.
• Learn about the emerging trends and technologies in seismic data interpretation, including cloud computing, big data, and advanced machine learning techniques.
• Explore how the integration of AI and digital twins is revolutionizing quantitative seismic interpretation and subsurface modeling.

Target Audience

This course is intended for professionals involved in seismic interpretation and reservoir characterization. The target audience includes:

• Geophysicists
• Reservoir Engineers
• Seismic Data Analysts
• Exploration and Production Managers
• Geologists
• Data Scientists and Engineers