Introduction to Machine Learning for Geophysical Applications
Instructor: J.C. (Jaap) Mondt, Breakaway
Disciplines: Data Science - Machine Learning
CPD Points: 10
Next deliveries: 21 June - 21 July 2021 (postponed) | 27 September - 27 October 2021 | 15 November - 15 December
|Early fee until
8 days before
|Regular fee from
7 days before
the course starts
|EAGE Bronze/Silver/Gold/Platinum Member||295 EUR||345 EUR|
|EAGE Platinum Member||295 EUR||295 EUR|
|EAGE Green Member||345 EUR||395 EUR|
|EAGE Bronze/Silver/Gold Student Member||150 EUR||175 EUR|
|EAGE Green Student Member||175 EUR||200 EUR|
||395 EUR||445 EUR|
|Education Package||2 credits||2 credits|
*EAGE Membership for the remainder of the year is included in the non-member fee
Format: this course consists of video lectures, reading material, quizzes and 4 live interactive webinars with the instructor. The live webinars will take place in the first two weeks of the course as indicated in the schedule below. During this time the instructor will be available for questions. After that, participants will have time to study independently, review the course materials and complete the assignments.
|Day 1||Introduction webinar||2 hrs|
|Day 2||Independent study|
|Day 3||Interactive Q&A webinar||2 hrs|
|Day 4||Interactive Q&A webinar||1 hr|
|Day 5 - 10||Independent study|
|Day 11||Conclusion webinar||1 hr|
|Day 12 - 30||Independent study|
Duration: 14 hours (click on "Register" to see the exact times of the next delivery)
Note this is an estimation of the time required to go through the course, including watching/reading lectures, attending webinars and completing quizzes.
After purchasing this course, you will have access to the intake quiz and introductory course materials. All course materials and assignments will be available after the course starts. You will have access to the course for a total period of 1 month after the start of the course. Make sure to complete all the requirements for the achievement of the certificate by this date.
A certificate of completion will be available upon completion of all course requirements. After the end of the course, your certificate will remain available for download in your Profile page.
Brief course description
The aim of the course is to introduce how Machine Learning (ML) is used in predicting fluids and lithology. It will give an understanding of the “workflows” used in ML. The used algorithms can be studied separately using references. Power-point presentations and videos will introduce various aspects of ML, but the emphasis is on computer-based exercises using open-source software.
The lectures and 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.
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 of Machine Learning discussed and applied to the case of predicting lithology and pore fluids.
All those interested in understanding the impact Machine Learning will have on the Geosciences and then as an example the impact on lithology and pore-fluid prediction. Hence, geologists, geophysicists and engineers, involved in exploration and development of hydrocarbon or mineral resources
A basic understanding of Geophysics and Statistics. A Pre-requirement quiz can be taken by participants to check whether their knowledge of Geophysics and Statistics is sufficient to follow the course.
About the instructor
I obtained a Bachelors in Geology at the University of Leiden, a Masters in Theoretical Geophysics at the University of Utrecht and a PhD in Theoretical Geophysics at the University of Utrecht.
My PhD in Utrecht was on “Full wave theory and the structure of the lower mantle”. This involved forward modelling of Pand S-waves diffracted around the core-mantle boundary and comparison of the frequency-dependent attenuation of the signal with those obtained from major earthquakes observed at long offsets in the “shadow zone” of the core. These observations were then translated into rock properties of the D” transition zone. After my PhD I joined Shell Research in The Netherlands to develop methods to predict lithology and pore-fluid based on seismic, petrophysical and geological data. Subsequently I worked in Shell Expro in London to interpret seismic data from the Central North Sea Graben. As part of the Quantitative Interpretation assignment I was also actively involved in managing, processing and interpreting Offshore Well Seismic Profiling experiments (from drilling rigs and production platforms). After my return to The Netherlands I headed a team for the development of 3D interpretation methods using multi-attribute statistical and pattern recognition analysis on workstations. After a period of Quality Assurance of “Contractor” software for seismic processing, I became responsible for Geophysics in the Shell Learning Centre. During that time I was in addition part-time Professor in Applied Geophysics at the University of Utrecht. From 2001 till 2005 I worked on the development of Potential Field Methods (Gravity, Magnetics) for detecting oil and gas. Finally I became a champion on the use of EM methods and became involved in designing acquisition, processing and interpretation methods for Marine Controlled Source EM (CSEM) methods. After my retirement from Shell I founded my own company (Breakaway), specialised in courses on acquisition, processing and interpretation of geophysical data (seismic, gravity, magnetic and electromagnetic data). In addition to still providing support to the Shell Learning Centre I give my own courses to International as well as National energy.