Facies prediction along the wellbore using Machine Learning
Facies prediction along the wellbore
using Machine Learning
Jaap C. Mondt
Duration one day (F2F) or one month (E-Learning)
Business context
More and more Machine Learning will play a role not only in
society in general but also in the geosciences. Machine Learning
resorts under the overall heading of Artificial Intelligence. In
this domain often the word "Algorithms" is used to indicate that
computer algorithms are used to obtain results. Also, "Big Data" is
mentioned, indicating that these algorithms need an enormous amount
of training data to produce useful results.
Many scientists mention "Let the data speak for itself" when
referring to machine learning, indicating that hidden or latent
relationships between observations and classes of (desired)
outcomes can be derived using these algorithms. A clear example is
in the field of Facies prediction. Often, we resort to statistical
relationships. Then Machine Learning enters into the game. From a
range of labelled logs (each depth sample has a facies label) we
can derive a linear/nonlinear relationship (model in ML
terminology) that predicts the label/facies (supervised learning).
Then the model can be applied to new logs. But sometimes it is
already useful if an algorithm can define separate clusters, which
then still need to be interpreted as facies (unsupervised
learning).
The Course
The aim of the one-day course is to introduce how Machine
Learning (ML) is used in predicting facies for a well. It will give
an understanding of the "workflows" used in ML. The used algorithms
can be studied separately using references. Power-point
presentations will introduce various aspects of ML, but the
emphasis is on computer-based exercises using open-source software.
The exercises deal with pre-conditioning the datasets (balancing
the input classes, standardization & normalization of data) and
applying several methods to classify the data: Bayes, Logistic,
Multilayer Perceptron, Support Vector, Nearest Neighbour, AdaBoost,
Trees. Non-linear Regression is used to predict porosity from other
logs
Who should attend!
All those interested in understanding the impact Machine
Learning will have on the Geosciences and then specifically the
impact on facies prediction on log data. Hence, geologists,
geophysicists and petroleum and reservoir engineers, involved in
exploration and development of hydrocarbon fields.
F2F course content
09:00-09:15 Welcome, Program, (1) Biography, (2)
Intro ML
09:15-09:30 (3) ML Tutorial, (4) ML Open Source
Software, (5) Weka
09:30-10:00 Exercise 1 (Classification)
10:00-11:15 Refreshments
10:15-11:00 (6) DNN
11:00-12:00 Exercise 2 (Comparison Algorithms)
12:00-13:00 (7) Activation Functions, (8) Forward
and Backward Propagation
13:00-14:00 Lunch
14:00-15:00 Videos: Geophysical Inversion versus ML,
Deeplizard
15:00-15:30 Exercise 3 (Very limited labelled data)
& 4 (Regression algorithms)
15:30-15:45 Refreshments
15:45-16:00 (9) ML Fluid Substitution
16:00-16:45 Exercises 5 (Multilayer Perceptron
Neural Networks)
16:45-17:00 (10) Future of ML in Geophysics
Learning, methods and tools
At the end of the course participants will have a clear idea how
Machine learning, being part of Artificial Intelligence will impact
the future of Geosciences. This will be evident from the examples
discussed. The course uses a mixture of lectures, practical
exercises and direct (workshop-like) participant involvement in
discussions.
Use of laptops for exercises and WIFI internet access in the
classroom is mandatory.
The course can be customized to meet specific needs
participants.
Email: j.c.mondt@planet.nl
Website: www.breakawaylearning.nl