Webinar and in-person: Machine Learning for Time Series and Anomaly Detection

Lunch will be served at 1030. 

Short abstract:

In this talk, we explore the power of machine learning on time series data, with a focus on two use cases: traffic prediction and wellbore annulus leakage detection in the oil and gas industry. Time series data can provide valuable insights in transportation, energy, and other fields. By leveraging machine learning techniques on time series data, we can make accurate predictions, uncover patterns, and gain valuable insights to drive evidence-based decision-making. In the context of traffic prediction, machine learning models can predict traffic patterns, optimize traffic management strategies, and improve the flow of traffic, leading to reduced congestion and enhanced quality of life.

In the oil and gas industry, the Intelligent Well Alarming Tool (iWAT) developed by Wintershall Dea utilizes various ML algorithms to accurately detect well integrity incidents and minimize the risk of safety and environmental incidents. By processing large amounts of multi-sensor data from oil and gas wells and identifying the most important sensors that caused anomaly detection using the Local Interpretable Model-agnostic Explanation (LIME) framework, iWAT represents an excellent use case for applying machine learning techniques to time-series anomaly detection.

Bio presenter 1:

Kay is a Geo/Data Scientist at Wintershall Dea Norge, with expertise in both geology and data science. He holds an MSc in Geology from the University of Bergen and has progressively upskilled himself in the domains of data science and machine learning throughout his career. Kay's professional interests lie at the intersection of geosciences, data science, and machine learning. He is passionate about leveraging domain knowledge in geosciences to develop and implement innovative data-driven solutions for subsurface characterization.

Bio presenter 2:

Tina, a Data Scientist at Wintershall Dea AG, holds a PhD in Computational Economics from the University of Cape Town. She began her Data Science journey as an ML expert for the GEMS Initiative at the University of Minnesota through a post-doctoral fellowship. Tina's professional interests center around leveraging AI foundation models to address complex business challenges.

FORCE seminars have previously been fully booked with waiting lists so you are encouraged to sign up as soon as you know you will attend.

Participation fees:

FORCE members: Free
Non-members: NOK 1000,- / 500,- (webinar)
University: Free
Student: Free

How a webinar works

You register as usual through the registration button above. Once you are registered you will get an invitation via email to join this webinar. 
FORCE uses Teams Video for this webinar, and has proven to work successfully. 

We recomment that everyone joining turn off their camera and microphone when joining.
If you have any questions you can use the chat or wait until the end of the talk.

FORCE seminars have previously been fully booked with waiting lists so you are encouraged to sign up as soon as you know you will attend.

Once you have registered, you will receive an outlook invitation with the Teams link. You will receive the link a few days prior to the webinar. 
You cannot forward the link to people who is not registered. 

Important information:
You can register as a FORCE member and pay "FORCE member" price if you are an employee of a member company.
All FORCE member companies are listed here.

Payment is made online by credit card. Please note that no refunds will be given after you have signed up. If you for any reason can not attend the workshop, you are welcome to send a representative, just inform Linn Smerud as soon as there are changes.