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Artificial Intelligence for Geoscience (Spanish Language Course)

Saturday, 7 November Saturday, 28 November 2020, 8:00 a.m.–5:00 p.m.  |  Virtual Short Course via Zoom (Bogota, Colombia time)

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Who Should Attend

Geologists, Geophysicists, Petroleum Engineers, and Mineral Resource Exploration and Exploitation professionals and students interested in strengthening their knowledge of Python programming and learn about its applications in Artificial Intelligence (Machine Learning and Deep Learning).

Course Dates:

November 7, 14, 21, 28

  • Total Academic Hours: 40
    • Lectures: 32 hours
      • Hours: 8 a.m. – 12 p.m. | 1 p.m. – 5 p.m.*
        *Times listed in Bogota time (GMT -5)
    • Exercises: 8 hours
    • CEUs: 3.2
Prerequisites
  • Fluency in Spanish
  • Basic / intermediate programming knowledge
  • Medium to advanced knowledge of geology and geophysics
Objectives

By the end of the course, participants should be able to:

  • Use the primary functionalities of Python and selected packages of the Python language (Numpy / SciPy / Pandas / Matplotlib / Seaborn), through a project in Google Colab
  • Apply the basic concepts of Artificial Intelligence and primary Artificial Intelligence algorithms, particularly in the areas of Machine Learning and Deep Learning applied to geoscientific data (electrical well logs, seismic, well production data, and geochemical data of minerals).
  • Apply geoscientific data analysis and visualization techniques using Python libraries
  • Interpret output obtained by the prediction models
  • Use libraries for Machine Learning (Scikit-Learn) and Deep Learning (Keras and TensorFlow)

An identical course will be offered in English at the beginning of 2021. Stay tuned to our website for more information.

Course Content

Artificial Intelligence for Geoscience equips participants with the theoretical and practical knowledge needed to apply Machine Learning and Deep Learning concepts to the field of geosciences.

Machine Learning is a subfield of Artificial Intelligence, which is based on trying to imitate the actions of human beings through the training of algorithms. This branch of Data Science is booming in various areas of geosciences, including electrical well log interpretation, reservoir characterization, seismic interpretation, identification of areas with high mining potential, among others.

Deep Learning focuses on the use of neural networks and applying the "back-propagation" method to adjust errors resulting from different iterations, and, when enough data is available, to obtain results far superior to those obtained by "classical" learning algorithms in Machine Learning.

This course will allow participants to apply the knowledge and algorithms learned immediately, both in their research, as well as in their professional career.

Course Structure
SESSION I: PYTHON BASICS (November 7)
  • Variables
  • Types of Data and Data Management
  • Definition and Execution of Functions
  • Primary Python Libraries:
    • Numpy
    • SciPy
    • Pandas
    • Matplotlib
    • Seaborn
  • Exploratory Data Analysis
  • Exercises:
    • Plot a geochemical dataset
    • Visualize, organize, and analyze an oil/gas production dataset
SESSION 2: APPLIED PYTHON (November 14)
  • Wavelet (Ricker) in time and frequency
  • Well logs
  • Display Geospatial Data (Mineral information)
  • Seismic volume load
  • Post-Stack seismic attributes calculation
  • Exercise:
    • Generate a Ormsby, Klander, Butterworth wavelet (Time and Domain)
    • Calculate Coherence Attributes using Python
SESSION 3: MACHINE LEARNING (November 21)
  • Supervised vs. Unsupervised
  • Regression:
    • Lineal
    • Logistic Regression
  • Classification:
    • KNN
    • SVM
  • Clustering
    • K- Means
    • Decision Trees
  • Dimension Reduction
    • Principal Component Analysis (PCA)
  • Exercise:
    • Facies Classification of Well Logs
    • Use Regression Methods to forecast production in an oil well
SESSION 4: DEEP LEARNING (November 28)
  • Programming a Neural Network (step by step)
  • Application of Neural Networks for facies prediction
  • Neural Networks for:
    • Missing Well logs
    • Production forecast of an oil well
  • Use CNN for object detection in seismic images
  • Exercise:
    • Use CNN to predict salt bodies in a seismic dataset
    • Classify 3D Seismic Facies using Deep Neural Networks
 
$US 295
Expires on
18 July, 3014
Nonmembers
$US 160
Expires on
18 July, 3014
AAPG Members
$US 50
Expires on
18 July, 3014
AAPG LACR Student Members*
 

*Limited number of spaces available by application only. Please CLICK HERE to apply for the discounted rate.

Roderick Perez
Emily Smith Llinás AAPG Latin America & Caribbean Region Manager
Diana Ruiz Vásquez AAPG Latin America & Caribbean Region Events Coordinator
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The American Association of Petroleum Geologists (AAPG) does not endorse or recommend any products and services that may be cited, used or discussed in AAPG publications or in presentations at events associated with AAPG.