Property Prediction using Neural Network Seismic Inversion in Aspen SeisEarth

This course offers a hands-on introduction to the Neural Network Inversion (NNI) workflow in Integrated Canvas. NNI is a technique used to predict rock and elastic properties from seismic attributes and log data. 

In this training, you will follow the four steps of the workflow to generate rock property volumes using angle stack volumes and well logs (density, P-impedance, S-impedance and Poisson's ratio). You will use QC tools within the workflow to fine-tune parameter settings and improve results. Additionally, the course offers a detailed overview of the theory behind NNI, along with guidance on defining and optimizing parameters.

Audience:

Geophysicist

Interpreters

Geologists

Training Details

  • Course Id:

    QSI106

  • Duration:

    0.5 day(s)

  • CEUs Awarded:

    0.4

  • Level:

    Intermediate

Approach

Instruction on basic and advanced topics

Discussion about the general approach

Instructor-guided demonstrations of features

Hands-on workshops that apply learned concepts

Detailed course notes

Pre-requisites

Agenda

The course covers the following topics:

1- Selecting the input data for the Neural Network Inversion (NNI) workflow

2- Creating the training set

3- Training the neural network and fine-tuning parameters

4- Validating and optimizing input data for NNI

5- Propagation: Generating rock and elastic property volumes using NNI

Register for a Class

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Aspen Technology, Inc. awards Continuing Education Units (CEUs) for training classes conducted by our organization. One CEU is granted for every 10 hours of class participation.