MATLAB accelerates the processing and analysis of both large-scale and real-time datasets including images and signals. It uses scalable and adaptive tools that integrate AI, high-performance computing, and signal and image processing capabilities. These features maximize data and image consumption and minimize turnaround times.
MATLAB is the integrated software platform for energy scientists and engineers to:
- Analyze ultra-large datasets with parallel computing and GPU acceleration
- Accelerate machine learning and deep learning to make data-driven decisions in real time
- Automate anomaly detection and recognition with image processing and computer vision
- Customize and deploy applications on IT infrastructure on-premises, in the cloud, or at the edge of the network
“High-performance computing with MATLAB enables us to process previously unanalyzed big data. We translate what we learn into an understanding of how human activities affect the health of ecosystems to inform responsible decisions about what humans do in the ocean and on land.”
Applications
Big data and image analysis in upstream energy
Streamline subsurface imaging with MATLAB
Accelerate large-scale seismic modeling and migration using full-waveform inversion (FWI) and reverse time migration (RTM) algorithms, enhanced by high-performance computing in MATLAB.
Streamline subsurface interpretation with MATLAB
Accelerate and automate seismic facies classification using wavelet signal decomposition. This method informs deep learning neural networks empowered by high-performance computing in MATLAB.
Accelerate subsurface data analysis with MATLAB
Discover how ARKCLS expedites subsurface data analysis by interconnecting digital interpretation systems and specialized software applications with MATLAB.
Big data and image analysis in downstream energy
Development of CRM for Reservoir Simulations Using PINNs
Discover how Chevron supports real-time decision-making during oil and gas production monitoring with reduced-order modeling, simulation, and machine learning in MATLAB.
Microseismic Digitalization at the Quest CCS Facility
See how Shell performs large-scale microseismic monitoring of a carbon capture and storage (CCS) process using an integrated app developed in MATLAB.
Accelerate Radar Simulations on NVIDIA GPUs Using GPU Coder
Learn how Shell automates image predictive analytics for event detection in chemical plants using deep learning and high-performance computing in MATLAB.
Resources
MATLAB for big data and image analysis
- Read and Combine Large Collections of Data with Datastore
- Working with Big Data in MATLAB (3:55)
- Define and Visualize Tall Arrays for Out-of-Memory Data
- MATLAB Tall Arrays in Action (4:13)
- Getting Started with MapReduce
- Using MapReduce Technique to Process 500GB of Server Logs (41:48)
- Resolve "Out-of-Memory" Errors
- Analyze Big Data from Sensors and IoT Devices (White Paper)
- Data Engineering for Engineering Data (46:01)
Parallel computing for big data and image analysis
Machine learning and deep learning for big data and image analysis
- Analysis of Big Data with Tall Arrays
- Big Data and Predictive Analytics at Shell (3:35)
- Big Data and Machine Learning for Predictive Maintenance (54:44)
- Deep Learning Toolbox Examples
- Signal Processing and Machine Learning Techniques for Sensor Data Analytics (42:45)
- Deep Learning and Machine Learning for Signal Processing Applications (33:07)
Products
Learn about the products used for big data and image analysis applications.
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