MathWorks Energy Symposium 2023
MathWorks® collaborates with global leaders to solve some of the most critical challenges facing the energy industry. The MathWorks Energy Symposium provides an opportunity for energy companies, service providers, and researchers to showcase and discuss state-of-the-art innovations using MATLAB® and Simulink® across upstream, midstream, and downstream.
The 2023 MathWorks Energy Symposium featured keynote speeches, technical presentations, technology demonstrations, and workshops in the areas of advanced data analytics and AI, digital twin modeling, simulation and deployment for operation optimization and predictive maintenance, and much more.
Driving Energy Innovation for a Balanced Planet
Learn the three engines of growth at SLB: core business, digital business, and new energy business. And how they innovate for a balanced planet.
AI in Engineered Systems for Energy Applications
AI is a key enabler for the Design, Development, and Operations of many engineered systems. This talk will summarize trends and recent advancements in AI that enhance the efficiency and safety of engineered systems in energy applications.
A Toolkit for Fault Stress Analysis and Natural Fracture Prediction
Learn more about the toolkit developed by Aramco to enable geoscientist to perform key structural geomechanics analysis in a computationally efficient and cost-efficient manner.
Learn ExploreDAS, an open-source MATLAB based Tool for modeling and imaging Fiber Optic Distributed Acoustic Sensing seismic data.
Deploying Models and Algorithms to the Asset, Edge & Cloud
Learn to how operationalize your models and algorithms and deploy for the asset, edge and cloud applications.
Find out how to leverage digital twin workflows, develop and deploy digital twins for oil & gas applications.
Seismic reservoir modeling and subsurface characterization in MATLAB
Learn the SeReM package that is developed for seismic reservoir modeling and subsurface characterization.
Applications of Machine Learning in Geothermal Reservoir Engineering
Learn why Machine Learning is important for digital oil field, the MATLAB Reservoir Simulation Toolkit (MRST) and some application examples.
