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

Elastic Strain Energy for Shearing Stresses01:20

Elastic Strain Energy for Shearing Stresses

316
As discussed in previous lessons, strain energy in a material is the energy stored when it is elastically deformed, a concept crucial in materials science and mechanical engineering. This energy results from the internal work done against the cohesive forces within the material. When a material undergoes shearing stress and corresponding shearing strain, the strain energy density, which is the energy stored per unit volume, is calculated. Within the elastic limit, where the stress is...
316
Residual Stresses01:26

Residual Stresses

310
Residual stresses reside in a structure even after removing the original stress inducer. This phenomenon often arises from varied plastic deformations across different parts of a structure. Consider a rod stretched beyond its yield point. It will not regain its original length due to permanent deformation. Even after load removal, the rod does not entirely lose stress because of uneven plastic deformations, resulting in residual stresses. The computation of these stresses in structures is...
310
Stress: General Loading Conditions01:15

Stress: General Loading Conditions

396
To grasp the intricacy of real-world conditions where multiple loads are applied simultaneously to a structure, one might visualize a section passing through a specific point within a body, aligned parallel to the xy plane. This section is subjected to various forces, including original loads, normal forces, and shearing forces.
The shearing force, possessing potential directionality within the plane of the section, is simplified into two component forces running parallel to the x and y axes....
396
Applications of Stress01:04

Applications of Stress

429
Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
The...
429
Stresses under Combined Loadings01:23

Stresses under Combined Loadings

254
When analyzing a bent tube with a circular cross-section subjected to multiple forces, it is crucial to determine the stress distribution in order to maintain structural integrity under varied load conditions.
The process begins by slicing the tube at critical points and analyzing the internal forces and stress components at these sections, focusing on the centroid. Normal stresses, generated by axial forces and bending moments, are either compressive or tensile and vary across the section from...
254
Stress-Strain Diagram01:10

Stress-Strain Diagram

942
A stress-strain diagram is a crucial tool that graphically displays a material's mechanical characteristics. This diagram is derived from a tensile test performed on a carefully prepared cylindrical specimen. The specimen has two gauge marks inscribed on its central part, and the distance between these marks is known as the gauge length. The cylindrical specimen is placed in a testing machine, which applies an increasing centric load. As this load grows, so does the gauge length. This...
942

You might also read

Related Articles

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

Sort by
Same author

A robust machine learning framework for predicting contact angle in nano-assisted chemical EOR.

Scientific reports·2026
Same author

Utilizing L-arginine as an eco-friendly absorbent for hydrogen sulfide mitigation in produced water.

Scientific reports·2026
Same author

Foam Stability Evaluation of a Biodegradable Surfactant with Green Polymeric Stabilizers for Underbalanced Drilling Fluids.

ACS omega·2026
Same author

Modeling Gas-Brine Surface Tension Using Data-Driven Techniques for Underground Hydrogen Storage: A Focus on Depleted Gas Reservoirs.

ACS omega·2026
Same author

Experimental study of CO<sub>2</sub> sequestration and H<sub>2</sub> generation potential through mineral carbonation in Saudi red mud.

Scientific reports·2025
Same author

Formation damage assessment in carbonate reservoirs after removing hematite-water-based filter cake using HCl.

Scientific reports·2025

Related Experiment Video

Updated: Oct 11, 2025

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.6K

Machine learning application to predict in-situ stresses from logging data.

Ahmed Farid Ibrahim1, Ahmed Gowida1, Abdulwahab Ali1,1

  • 1College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.

Scientific Reports
|December 7, 2021
PubMed
Summary

Machine learning models accurately predict minimum and maximum horizontal stresses using well-log data. Adaptive Neuro-Fuzzy Inference System (ANFIS) showed superior performance for minimum horizontal stress prediction.

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.7K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Related Experiment Videos

Last Updated: Oct 11, 2025

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.6K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.7K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Area of Science:

  • Geophysics
  • Petroleum Engineering
  • Machine Learning Applications

Background:

  • In-situ stress determination is crucial for subsurface engineering tasks like well planning and hydraulic fracturing.
  • Traditional methods for estimating minimum (σh) and maximum (σH) horizontal stresses are often complex, costly, or require unavailable data.
  • Overburden stress (σv) is easily determined from density logs, but horizontal stresses pose a significant challenge.

Purpose of the Study:

  • To investigate the application of machine learning (ML) techniques for predicting in-situ horizontal stresses (σh and σH).
  • To compare the performance of Random Forest (RF), Functional Network (FN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting σh and σH using well-log data.

Main Methods:

  • Utilized well-log data including gamma-ray (GR), bulk density (RHOB), and compressional/shear wave transit times (DTC, DTS).
  • Developed and trained RF, FN, and ANFIS models using a dataset of 2307 points from two wells.
  • Validated the developed ML models using data from a separate well to assess predictive accuracy and robustness.

Main Results:

  • All three ML models demonstrated a strong capability to accurately predict σh and σH from well-log data.
  • ANFIS achieved the highest correlation coefficient (R=0.96) for predicting σh on the validation dataset, outperforming RF (R=0.91) and FN (R=0.88).
  • For σH prediction, all models showed high accuracy (R>0.98) with low average absolute percentage error (AAPE<0.3%), and captured stress trends with depth.

Conclusions:

  • Machine learning offers a robust and cost-effective approach for predicting in-situ horizontal stresses using readily available well-log data.
  • The study confirms the efficacy of ML techniques, particularly ANFIS, in overcoming the challenges associated with traditional stress determination methods.
  • These findings support the integration of ML into subsurface planning and analysis, reducing the need for expensive site investigations.