Facies prediction along the wellbore using Machine Learning

Trainer(s): Jaap 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).

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.

Course content

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

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.

Day by day programme

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


Email: j.c.mondt@planet.nl

Website: www.breakawaylearning.nl