Video and Webinar Series

MathWorks Energy Conference 2022

MathWorks collaborates with global leaders in the energy industry to solve some of their most critical challenges. This conference provides an opportunity for a knowledge exchange between researchers and industry practitioners to highlight and discuss state-of-the-art innovations using MATLAB® and Simulink® in the areas of drilling, petroleum engineering, and geosciences.

The 2022 MathWorks Energy Conference features presentations, panel discussions, technologies, and success cases in the areas of IIoT, data analytics, AI, and both data-driven and physics-based modeling for digital twin applications, which are major technical disciplines driving the future of oil and gas.

Digital Solutions for Downhole Product Development As part of its digital transformation journey, SLB took a holistic approach to the development of advanced digital solutions using MathWorks solutions for end-to-end workflows in core and future energy technology applications.

Cloud and Container Integration Options for Operationalizing MATLAB Analytics Discover how to run MATLAB analytics in the cloud to speed up computation and to deploy algorithms in a production environment.

Scalable Validation of Time-Series Data from Drilling Operations in AI-Based Decision Systems Using Statistics and Machine Learning Toolbox and Deep Learning Toolbox in MATLAB and Docker container capabilities, learn how to deploy validation models for automated validation of time-series data.

How Python and MATLAB Can Work Together Combining MATLAB and Python, you can take advantage of the best capabilities of each environment. This enables you to reuse existing code, incorporate functionality, and collaborate with colleagues working in another language.

Design Reduced Order Models (ROMs) For Oilfield Processes in MATLAB Learn how to build a digital twin for wellbores using a data-driven modeling approach.

Development of CRM for Reservoir Simulations Using PINNs Learn how a reduced-order model is built using the time-series production data from a real oil and gas field. The CRM is chosen as a reduced-order representation for the reservoir simulator.

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