EAGE Extensive Online Courses

Title: Geological CO2 storage

Instructors:
- Andreas Busch (Heriot-Watt University)
- Eric Mackay (Heriot-Watt University)
- Florian Doster (Heriot-Watt University)
- Martin Landro (NTNU)
- Philip Ringrose (Equinor, NTNU)

Contributors Module 7:
- Tip Meckel (University of Texas at Austin)
- Elisabeth Holuscha (Plan-Wissenschaft)

Level: Foundation

CPD points: 15

Format: this extensive and interactive course consists of videos, presentations, (reading) assignments, quizzes and 7 live webinars with the instructors

Dates: 10 October - 19 December 2023

Duration: 24 hours

Note this is an estimate of the time required to go through the course, including watching/reading lectures and attending webinars.

After purchasing this course, you will have access to the intake quiz and introduction material. Lectures and exercises will become available from 10 October 2023 as per the schedule above. You will have access to the course material until 19 December 2023. Make sure to complete all the requirements for the achievement of the certificate by this date.

Certificate
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.

Course outline

Module 1: Introduction
- Introduction to CCS
- Introduction to saline aquifer storage
- CO2 storage project design

Module 2: Reservoir concepts and storage requirements
- Reservoir/seal systems for pore space storage
- Storage capacity
- CO2 PVT

Module 3: Fluid mechanics (part I)
- Single phase flow in porous media - Darcy
- Single phase flow in porous media - Mass conservation
- Two-phase transport - Pore scale processes

Module 4: Fluid mechanics (part II)
- Two-phase transport: Introduction to relative permeability, capillary pressure
- Two phase transport: Non-linear processes
- Link to dynamic reservoir modelling/simulation

Module 5: Storage risks: Seals, assessment, geomechanics and geochemistry
- Geochemical requirements to safely store CO2
- Seal integrity

Module 6: CCS monitoring and risk assessment
- Well integrity and subsurface monitoring
- Seabed/shallow subsurface monitoring
- (Near) Surface and Marine monitoring

Module 7: Public perception, policy and emerging/niche CO2 storage options
- CO2 for enhanced oil production
- Emerging/niche options to store CO2
- Public perception and policy

Intended Audience

All those interested in the geoscience and engineering aspects or carbon capture and storage.

Pre-requisites

None

By purchasing this course you agree to Terms and Conditions for Registration within EAGE. 

Title: Introduction to Machine Learning for Geophysical Applications Instructor: J.C. (Jaap) Mondt, Breakaway Disciplines: Data Science - Machine Learning Level: Foundation CPD points: 10 Format: this course consists of video lectures, reading material, quizzes and 4 live interactive webinars with the instructor. The webinars will take place in the first two weeks as per the schedule below. During this time the instructor will be available for Q&A. Participants will then have time for independent study. Dates: 13 November - 13 December 2023.  Registration Early feeuntil 8 days before the course start Regular fee from 7 days before the course start EAGE Bronze/Silver/Gold Member  325 EUR  375 EUR EAGE Platinum Member   325 EUR   325 EUR  EAGE Green Member   385 EUR  435 EUR EAGE Bronze/Silver/Gold Student Member  150 EUR   175 EUR  EAGE Green Student Member  175 EUR  200 EUR Non Member*  465 EUR  515 EUR Education Package   2 credits   2 credits  *EAGE Membership for the remainder of the year is included in the non-member fee Buy Education Package    Join EAGE today! Schedule Date   Time   Description 13 November 2023   15:00 - 18:00 CET   Introduction webinar 14 November 2023   15:00 - 18:00 CET    Q&A webinar 24 November 2023   15:00 - 18:00 CET   Q&A.  End of the course Duration: 14 hours Note this is an estimate 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 course material. Your access will expire at the end of the course. Make sure to complete all the requirements for the achievement of the certificate before this date.  CertificateA 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. Short 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. Topic covered: 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. Intended Audience 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 Pre-requisites 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. By purchasing this course you agree to Terms and Conditions for Registration within EAGE.