Face-to Face Course

August 5 to 9, 2024

Duration : 5 days

Level : Basic

WHO SHOULD ATTEND:

Seismic interpreters who wish to extract meaningful information from their data, seismic processors who want to find out different ways to characterize the formations of their interest, stratigraphers and structure geologists who use 3D seismic volumes to prepare detailed reservoir models, reservoir engineers who want to understand the use of seismic input to add detail to 3D reservoir models, and students of geophysics who wish to become qualified interpreters.

OBJECTIVES:

The seismic interpretation of today is more sophisticated and comprehensive. Modern seismic data acquisition, processing, and imaging, with long offset, wide azimuth data is yielding higher resolution, and even with conventional workflows leads to improved quantitative interpretation. Conventional workflows can be improved upon by bringing in changes which can result in value addition to achieve the objective for a project. Example changes could be the use of a calibrated well-driven velocity field in place of the velocity field obtained from processing of seismic data, bringing in appropriate poststack processing steps for preconditioning noisy prestack seismic data from areas high-velocity near surface geological formations impact the quality of seismic data, generating more accurate low-frequency impedance models for prestack simultaneous impedance inversion using multiattribute analysis, and even using different facies trends for inversion of seismic data for multi-level pay formation as in the Permian Basin. 

Successful implementation of such workflows could result in fully integrated, three-dimensional characterization, and information on reservoir heterogeneity. In such cases reservoir properties estimated from seismic data in the form of lithology and petrophysical properties are found to be more realistic. 

Additionally, utilization of machine learning workflows contributes to an improved level of understanding of the reservoir in terms of lithofacies and the overall heterogeneity. Application of advanced visualization tools aids such understanding. 

In this course, beginning with the introduction to various kinds of attributes, example workflow applications will be demonstrated for seismic impedance inversion, and for deterministic and probabilistic machine learning facies classification that enable an integrated seismic interpretation and reservoir characterization.

BENEFITS:

After attending this course, the participants will be able to:

• Display multiple attributes in a single image
  • Identify the geological features highlighted by spectral decomposition and wavelet transforms
  • Interpret spectral anomalies in the context of thin bed tuning
  • Evaluate the use of spectral information as a direct hydrocarbon indicator
    • Evaluate alternative algorithms to calculate volumetric dip and azimuth in terms of accuracy and lateral resolution
• Exploit changes in amplitude variation with offset and azimuth to produce multiple-attribute images that illuminate different geologic features of interest
• Interpret attributes computed from multiple azimuth-limited seismic volumes to better characterize faults and fractures
• Use coherence and other attributes to quality control the choice of processing parameters
• Recognize acquisition footprint on seismic attribute time and horizon slices
• Identify the limits to vertical and lateral resolution
  • Identify the limits of attribute analysis on data that have been poorly imaged
• Use inversion to integrate a user-defined earth model and synthetic seismograms to ‘best fit’ the measured seismic data
• Differentiate and ideally choose between alternative local, global, sparse-spike, or geostatistical inversion algorithms, between local and global inversion solutions
• Use elastic inversion effectively as a lithologic indicator.
• Characterize reservoirs effectively with the use of seismic data
• Answer questions such as: Can I observe the reservoir on seismic? How large is the reservoir? How does seismic data acquisition and processing impact the interpretation of my reservoir? What are the limitations and pitfalls of the workflows/techniques applied? Will I be able to effectively interpret reservoirs in producing hydrocarbon areas?

Course content includes:

Meaning of different seismic attributes, their advantages and in what contexts they are used.

Different types of coherence attributes (conventional, multispectral, multiazimuth, and multioffset coherence), algorithms, performance comparisons based on computational methods, preconditioned seismic data, their enhancement based on creative workflows, and calibration with well data.

Different types of curvature attributes, their multispectral measures, calibration with well data, generation of 3D rose diagram volumes, and fracture prediction from scaled curvature attributes.

Spectral decomposition of seismic data, different methods, limitations, RGB blending of frequency volumes, voice components and their applications, phase decomposition and its applications, both phase and spectral decomposition as direct hydrocarbon indicators.

Use of spectral balancing and bandwidth extension (sparse-layer seismic reflectivity inversion) for the generation of seismic attributes and their applications.

AVO analysis, classes of AVO, approximations to Zoeppritz equations, importance of amplitude-friendly process sequences, modeling of seismic gathers and their comparison with seismic gathers, AVO techniques and indicators, impact of noise, crossplotting and visualization of AVO attributes.

Impedance inversion and its benefits, different methods for poststack, prestack impedance inversion including joint impedance inversion (using multicomponent seismic data), geostatistical inversion, elastic impedance, extended elastic impedance inversion and application for determination of petrophysical properties.

Seismic attribute applications for shale resource plays, geothermal reservoirs, gas storage and carbon sequestration zones.

Application of machine learning methods for seismic facies classification and automatic fault interpretation