Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

175
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
175
Random Sampling Method01:09

Random Sampling Method

10.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
10.9K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

84
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
84
Random Variables01:09

Random Variables

11.2K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
11.2K
Sampling Distribution01:12

Sampling Distribution

11.6K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
11.6K
Sampling Methods: Overview01:06

Sampling Methods: Overview

221
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
221

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effect of hypergravity on the biomechanics of the musculoskeletal system in human lumbar spine: a numerical study.

Frontiers in bioengineering and biotechnology·2026
Same author

H<sub>2</sub>O<sub>2</sub>-independent oxygen activation via proton-coupled electron transfer for selective hydroxyl radical generation.

Water research·2026
Same author

Recyclable glass fiber-reinforced epoxy copper clad laminates for printed circuit board.

Communications chemistry·2026
Same author

Associations of Mycoplasma pneumoniae load, co-infections, and macrolide resistance with clinical-laboratory profiles in hospitalized pediatric pneumonia: a targeted next-generation sequencing study of bronchoalveolar lavage fluid.

Annals of clinical microbiology and antimicrobials·2026
Same author

Comparative analysis of lipid-lowering mechanisms in efficacy-divergent traditional Chinese medicines.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Sensitivity of various material properties to the intervertebral disc biomechanics.

Computer methods in biomechanics and biomedical engineering·2026

Related Experiment Video

Updated: May 7, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

318

Reservoir Stochastic Simulation Based on Octave Convolution and Multistage Generative Adversarial Network.

Xuechao Wu1, Wenyao Fan2, Shijie Peng3

  • 1School of Computer and Information Engineering, Hubei Normal University, Huangshi, 435002, China. wxc2201710237@126.com.

Scientific Reports
|December 31, 2024
PubMed
Summary

This study introduces OctSinGAN, a novel hybrid framework for advanced reservoir simulation. It effectively reproduces complex geological features using multiscale analysis and octave convolutions, improving spatial resolution and accuracy.

Keywords:
Generative adversarial networksJoint loss functionMulti-scale spatial representationOctave convolutionReservoir units

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Generation of Local CA1 &#947; Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.1K

Related Experiment Videos

Last Updated: May 7, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

318
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Generation of Local CA1 &#947; Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.1K

Area of Science:

  • Geosciences
  • Artificial Intelligence
  • Reservoir Engineering

Background:

  • Traditional stochastic simulation methods struggle with complex reservoir units due to computational limitations and low spatial resolution.
  • Generative Adversarial Networks (GANs) offer potential for high-order statistical fitting of spatial variables but face challenges with limited training data and overfitting when using stacked Convolutional Neural Networks (CNNs).

Purpose of the Study:

  • To develop an advanced reservoir simulation framework that overcomes the limitations of traditional methods and existing GAN-based approaches.
  • To improve the accuracy and efficiency of reservoir characterization for complex geological formations.

Main Methods:

  • A hybrid framework, OctSinGAN, combining octave convolution and a multi-stage GAN, was proposed for reservoir simulation.
  • A pyramid structure was employed for multiscale representation using a single Training Image (TI), capturing features at various scales.
  • Octave convolution was utilized for multi-frequency feature representation, and a joint loss function optimized network parameters.

Main Results:

  • The OctSinGAN framework demonstrated effective reproduction of spatial variability, channel connectivity, and spatial structures across three different Training Images.
  • Simulations generated by OctSinGAN showed high similarity to the original Training Images, indicating robust performance.
  • The proposed method addresses issues of insufficient training samples, high computing consumption, and overfitting associated with previous methods.

Conclusions:

  • OctSinGAN provides a powerful and effective approach for high-fidelity reservoir simulation, particularly for complex geological structures.
  • The framework enhances reservoir characterization by accurately capturing multiscale features and improving simulation quality.
  • This advancement in reservoir simulation holds significant implications for subsurface modeling and resource management.