31 August, 2022 Tulsa Oklahoma United States Virtual

Artificial Intelligence for Oil and Gas Using Python

1-31 August 2022
  |  
Virtual Event
Who Should Attend
This course is designed for geology, geophysics, petroleum engineering, and mineral resources exploration and development professionals and students interested in strengthening their knowledge of Python programming and learning about its applications to Artificial Intelligence (Machine Learning and Deep Learning).
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, TensorFlow and PyTorch)
Course Content

Artificial Intelligence for Oil and Gas Using Python equips participants with the theoretical and practical knowledge needed to apply Machine Learning and Deep Learning concepts to the fields of geosciences and engineering.

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 Format

Artificial Intelligence for Oil and Gas Using Python includes four self-paced independent study modules, along with three interactive working sessions with the instructor.

The format allows participants to work at their own pace and to reach out to the instructor for support throughout the week and during the live sessions.

Course modules will be available for viewing on the website from August 1- 31, 2022. Live sessions take place over three Saturdays, on August 6, 13 and 27, from 8 a.m. – 12 p.m. CDT/COT (GMT -5).

Course modules will be delivered in English, and the instructor will conduct working sessions in English and Spanish, accommodating participants’ preference.

Course Modules
Session 1: Python Basics
  • 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 in Geology and Geophysics

Wavelet (Ricker) in time and frequency

  • Well logs
  • Display Geospatial Data (Mineral information)
  • Seismic volume load
  • Post-Stack seismic attributes calculation
  • Exercises:
    • Generate a Ormsby, and Butterworth wavelet (Time and Domain)
    • Calculate Coherence Attributes using Python
Session 3: Machine Learning
  • Supervised vs. Unsupervised
  • Regression:
    • Lineal
    • Logistic Regression
  • Classification
    • KNN
    • SVM
  • Clustering
    • K- Means
    • Decision Trees
  • Dimension Reduction
    • Principal Component Analysis (PCA)
  • Exercises:
    • Facies Classification of Well Logs
    • Use Regression Methods to forecast production in an oil well
Session 4: Deep Learning
  • 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
  • Exercises:
    • Use CNN to predict salt bodies in a seismic dataset
    • Classify 3D Seismic Facies using Deep Neural Networks
Working Session Schedule

August 6
Python Basics & Applied Python

August 13
Machine Learning

August 27
Deep Learning

AAPG Headquarters
1444 S Boulder Avenue
Tulsa Oklahoma 74119
United States
+1 918 584 2555
Tulsa, OK - AAPG Tulsa, OK - AAPG Virtual 63511 AAPG Headquarters

$195
$195
Expires on
31 August, 2022
AAPG/ACGGP Members
$295
$295
Expires on
31 August, 2022
Non-Members
$45
$45
Expires on
31 August, 2022
AAPG/ACGGP Student Members
$95
$95
Expires on
31 August, 2022
Student Non-Members
Registration fee includes:

Access to four independent study modules and exercises, three live sessions with instructions, digital course notes, and certificate of participation upon completion of the course.

Participation Limit:

Professional participants: 40
Student participants: 40

Registration Deadline:

25 July 2022


Payment Information:
  • All prices are listed in USD.
  • Payment on the AAPG website is available for individuals using a credit or debit card.
  • To pay via wire transfer or to make a single payment for multiple registrations, please contact Diana Ruiz, AAPG Latin America and Caribbean Region Events Coordinator, at [email protected].
  • To pay in Colombian pesos using a Colombian bank account please contact Ana María Ramírez, [email protected]
Refund/Cancellation policy:
  • No refunds or cancellations will be issued one month prior to the course
  • Registrants who are unable to participate may designate a substitute to attend in their place

Cancellations and substitution requests should be sent to [email protected]

Technical Requirements :
  • Personal computer or device with high-speed internet access
  • Access to Zoom platform through Internet browser or computer application
Roderick Perez Altamar University of Vienna
Desktop /Portals/0/PackFlashItemImages/WebReady/perez-altamar-roderick-jun2022.jpg?width=75&quality=90&encoder=freeimage&progressive=true 422
Technical Professional - ACGGP
Desktop /Portals/0/PackFlashItemImages/WebReady/becerra-laura.jpg?width=75&quality=90&encoder=freeimage&progressive=true 63528 Laura Becerra
AAPG Latin America & Caribbean Region Events Coordinator
Desktop /Portals/0/PackFlashItemImages/WebReady/ruiz-vasquez-diana.jpg?width=75&quality=90&encoder=freeimage&progressive=true 49564 Diana Ruiz Vásquez

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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.