Join the Upcoming Free PBL Session
PBL
Shell Project
AI for Energy and Sustainability
Leverage machine learning to predict and mitigate seismic activity, enhancing safety and sustainability in subsurface energy operations.
Project
Shell Project
Location
Online
Duration
8 Weeks
Upcoming Sessions
Spring 2026
Outcomes
AI in Energy exploration for resource identification
AI-driven decision making for sustainable energy solutions
Feature extraction from structured and unstructured datasets
Carbon sequestration modeling for CO₂ storage tracking
Causal inference between energy production and seismicity
You Will Get
Industry Guidance
Work directly with our project leads—experts and top researchers—who bring their real-world insights and expertise straight to your learning experience.
Research Experience
Collaborate with teammates and the project lead in a multi-week project to pursue novel questions in your research field.
Peer Networks
Engage with our PBL participants from all over the world. Collaborate with new peers and learn about their own research endeavours.
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A Strong Portfolio
Put your best foot forward in the PBL with a standout project and receive a PBL Evaluation Report that can be used as a recommendation letter for employers and grad schools.
Expert Guidance
Get personalized feedback to grow your research and innovation skills.
Deliverables
Real projects, lasting connections, and new opportunities beyond your program.
Project Deliverables
The final presentation of your 8 weeks could be a poster, written report, or a slide deck, all of which can be expanded on.
Research Extension
Utilize up to 5 additional meeting times with the project lead after the project’s conclusion to build your work out for publication or conference presentation.
Industry Network
Meet peers in your projects and participate in a global talent community both online and in-person.
Industry Application
Shell, a leader in the energy sector, invests in innovative technologies to enhance energy production and sustainability. This PBL aligns with Shell’s focus on mitigating environmental impacts and optimizing subsurface energy operations using advanced machine learning techniques. By predicting and managing seismic activity, Shell can ensure safer and more efficient energy production, aligning with their sustainability goals.
Popular Industry Positions
Carbon Sequestration Analyst
Use AI to monitor and predict CO₂ storage efficiency.
Energy Data Scientist
Develop AI models for resource exploration and environmental monitoring.
Geothermal Engineer
Optimize geothermal energy production and assess seismic risks.
Tracks
Track 1
The Wildcatter of Modern Times – Using AI to Find Natural Resources
Learners will analyze subsurface datasets and build machine learning models to identify potential oil and gas reserves and predict economic viability.
Analyze structured and unstructured subsurface data to detect resource indicators.
Develop machine learning models to identify anomalies signaling natural resources.
Integrate economic modeling to predict the financial feasibility of extraction.
Gain hands-on experience with data processing, feature engineering, and AI in geoscience.
Understand the role of AI in modern resource exploration and energy investment decisions.
Track 2
AI-Driven Geothermal Energy Production
Learners will apply machine learning techniques to forecast seismic activity and optimize geothermal energy production.
Train AI models to predict earthquake activity based on operational parameters.
Use optimization algorithms to adjust operations in response to real-time seismic data.
Analyze historical geothermal production data to improve efficiency and safety.
Explore AI applications in sustainable and renewable energy management.
Develop data-driven decision-making skills for real-world energy industry challenges.
Track 3
AI-Driven Assessment of Environmental Impact
Learners will analyze datasets on oil and gas production and seismic activity to determine whether human activity is influencing earthquake patterns.
Extract and analyze time-series data from environmental and industrial datasets.
Develop machine learning models to assess interdependencies between seismicity and energy production.
Identify causal relationships between natural tectonic activity and industrial operations.
Understand AI’s role in environmental monitoring and regulatory compliance.
Gain expertise in AI applications for sustainability and risk mitigation in energy production.
Track 4
AI-Powered Carbon Sequestration Analysis
Learners will develop AI models to predict and track the movement of CO₂ stored underground for carbon sequestration efforts.
Process and analyze subsurface sensor data from long-term CO₂ storage sites.
Develop machine learning models to predict CO₂ plume behavior underground.
Integrate multiple data sources to improve storage monitoring and risk assessment.
Explore AI-driven strategies for carbon capture and sustainable energy solutions.
Contribute to advancements in climate change mitigation through AI applications.
PBL Journey
Online PBL Projects meet once a week for 8 weeks, and follow the research project format. Participants will meet the project lead, learn the conventions of the field and familiarize themselves with the tracks, then spend the middle portion of their time collaborating to develop their research.
At the end, participants will present their final project and receive feedback, with the opportunity to extend their timeline and develop the project in greater depth.
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Project Team
Our Academic Team plays a vital role in your PBL journey at Blended Learning. We are dedicated to enhancing your learning experience and ensuring your academic success. Our team consists of three distinct roles, each with a specific focus to support your Research Guidance, Project Progress, and Personal Growth.
Project Lead
Providing Industry and Research Guidance
Researcher at MIT’s Civil and Environmental Engineering Department and Harvard’s Earth and Planetary Sciences Department
He focuses on understanding earthquakes and mitigating their hazards to infrastructure and lives. Prior to MIT, he worked as a Geophysicist in the oil and gas industry, researching methods to image and characterize subsurface geological structures for oil and gas production, geological carbon sequestration, and geothermal energy exploration. He has published peer-reviewed papers in high-impact journals like Geophysical Prospecting and The Leading Edge, and has presented at AGU conferences on earthquake hazards from anthropogenic energy and environmental subsurface activities.
Academic Advisor
Tracking Your Project Development
The Academic Advisor is dedicated to your project completion success. They manage the progress of your PBL, guiding team formation, facilitating group discussions, and resolving conflicts. Additionally, the Academic Advisor ensures team member contributions are on track and provides logistical support, including attendance tracking, hosting recitation sessions, managing research support requests, and conducting student evaluations at the end of the PBL.
From Our Students
"After a night spent debugging, I suddenly discovered the program running perfectly. In that triumphant moment, you realize your true capability and success. The exhaustion fades, replaced by the thrill of knowing your skills and persistence led to this achievement, reaffirming your potential."
Nicole Y.
National University of Singapore
B.S. Economics
FAQs
What is the learning format of a PBL?
All PBLs are offered in an 8-week online format that begins with an orientation followed by subject setup overview of the different tracks. The majority of the session time is dedicated to project development, with a final presentation at the culmination of the 8 weeks. Many PBLs are also offered bi-annually in an on-campus format that consists of daily in-person meetings.
How long does each PBL cohort last？
One round of the Online PBL cohort lasts 8 weeks, preceded bys a pre-PBL orientation week. Each On-Campus PBL usually has 8 in person meetings, with intensive classroom education and collaboration. This means the biggest difference between online and on-campus PBLs is time participants have in between meetings.
How can I be more academically prepared before the PBL starts?
Review the Blended Learning Insights sent by the Academic Advisor and familiarize yourself with the project topic and pre-learning materials. Ensure you have all necessary softwares and other resources needed for the PBL.
For each PBL cohort, will I work in teams? Are PBL team members self-selected or assigned?
Yes, you will work in teams for each round of the PBL Cohort. Each team has 3 to 6 participants, organized by the Academic Team. The Academic Advisor will organize groupings based on students' backgrounds, preferred track, and skills.
Can I work with the Project Lead on my project after the PBL ends?
Yes, with your AI + X Research Plan, you may request up to five PBL Research Extension meetings, where you work with the project lead to develop your project into a working manuscript. To schedule a PBL Research Extension meeting, talk to your Academic Advisor at the conclusion of your PBL.
What do I receive at the end of the PBL?
At the conclusion of the PBL cohort, you can request a PBL Evaluation Report which summarizes the PBL content, the hours you spent, the track you chose, and includes a recommendation letter from the Project Lead (for eligible participants who completed the project successfully).
Is attendance mandatory for PBL Live Sessions and Recitation Sessions?
Yes, attendance is mandatory for both PBL Live Sessions and Recitation Sessions. Participants with three or more unexcused absences forfeit their eligibility for a PBL Evaluation Report.
Do I need to have my camera on during online PBL Live Sessions?
Yes, you must have your camera on during online PBL Live Sessions. Participants with cameras off will be marked as absent. This is meant to encourage active engagement and participation in meetings.
