Energy Resources

Energy Resources Modeling and Simulation with MATLAB and Simulink

MATLAB and Simulink let you customize and speed up the testing and evaluation of upstream and downstream processes via dynamic modeling and simulation. These capabilities can optimize asset performance and production with minimum operating costs and maximum returns on investment.

With MATLAB and Simulink, you can:

  • Customize and scale up 3D design, modeling, and simulation of subsurface and surface processes in conventional, unconventional, or storage reservoirs
  • Analyze seismic and wellbore data in multiple domains using image, signal, and wavelet processing algorithms
  • Speed up large-scale data analysis using computer vision (image and signal processing) and data science (AI, machine learning, and deep learning) with high-power computing (HPC) capabilities
  • Interconnect MATLAB and Simulink with external software applications, create your own application, and automatically generate code as required

Energy Resources Products Developed in MATLAB and Simulink

With MATLAB and Simulink, you can customize modeling and simulation of conventional, unconventional, carbon capture and storage (CCS), and new energy processes using:

Data Science and HPC Toolsets: Developed in MATLAB, these toolsets provide digital technology solutions with customizable toolboxes in various areas:

  • AI: Machine learning, deep learning, reinforcement learning
  • HPC: Parallel, GPU, cloud, and quantum computing; production server
  • IPCV: Image, signal, and wavelet processing; computer vision; GIS

Upstream Products: MATLAB supports geoscientists and engineers in subsurface and surface process modeling and simulation:


Subsurface Modeling and Simulation Applications with MATLAB

Subsurface Modeling with SeReM

Model and classify reservoir facies using rock property modeling and seismic inversion algorithms.

Subsurface Simulation with MRST

Model and simulate complex dynamic reservoirs properties using compositional fluid dynamics.

Data Science Applications with MATLAB

MATLAB for Data Science

Explore data, build machine learning models, and do predictive analytics.

Big Data with MATLAB

Explore, analyze, and develop predictive models on big data.

Seismic Facies Classification with Deep Learning and Wavelets (54:28)

Watch how applying signal processing techniques before AI algorithms helped win the SEAM AI Applied Geoscience GPU Hackathon.

High-Power Computing Applications with MATLAB

Parallel (CPU and GPU) Computing with MATLAB and Simulink

Perform large-scale computations and parallelize simulations using multicore desktops, GPUs, clusters, and clouds.

Cloud Computing with MATLAB and Simulink

Speed up development processes with on-demand access to enhanced compute resources, software tools, and reliable data storage.

Quantum Computing with MATLAB and Simulink

Build, simulate, and run quantum algorithms with MATLAB Support Package for Quantum Computing.

Image Processing and Computer Vision Applications with MATLAB

Image Processing Toolbox

Perform image processing, visualization, and analysis.

Signal Processing Toolbox

Perform signal processing and analysis.

Wavelet Toolbox

Perform time-frequency and wavelet analysis of signals and images.

Computer Vision Toolbox

Design and test computer vision, 3D vision, and video processing systems.

Seismic Raster to SEG-Y Converter

Convert seismic images into georeferenced SEG-Y format files.

Examples of AI, High-Power Computing, and Image Processing and Computer Vision Applications

Shell Geologists Develop and Deploy Software for Predicting Subsurface Geologic Features

Shell develops an application for quantitatively characterizing subsurface geologic features to reduce oil and gas exploration costs.

Sinopec Develops High Accuracy Intelligent Seismic Inversion with Deep Learning

Sinopec engineers use MATLAB to introduce a new seismic inversion method called frequency-phase intelligent inversion.

Seismic Dip Guided Horizon Interpretation in Petrel with MATLAB (9:50)

Chevron integrates MATLAB with Petrel to design and implement a seismic dip guided horizon auto-tracking algorithm.