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

Modeling and Similitude01:12

Modeling and Similitude

859
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
859
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

980
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
980
Typical Model Studies01:30

Typical Model Studies

842
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
842
Design Example: Design of an Irrigation Channel01:27

Design Example: Design of an Irrigation Channel

1.3K
Trapezoidal channels are widely used in irrigation systems due to their cost-effectiveness and efficiency in conveying water. Trapezoidal channels feature a flat bottom and sloping sides, making them stable and easier to construct compared to other shapes. The bottom width and side slope ratio are determined based on the required flow capacity and site conditions. The side slope is kept gentle for unlined channels to prevent soil erosion.Hydraulic parameters in channel design include the flow...
1.3K
Methods of Medium Optimization01:28

Methods of Medium Optimization

70
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

391
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
391

You might also read

Related Articles

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

Sort by
Same author

Neural network and regression approaches for predicting bubble point pressure in oil reservoirs.

Scientific reports·2026
Same author

A numerical and experimental approach to oil recovery performances during combined xanthan gum and carbon dioxide flooding.

Scientific reports·2026
Same author

Learning based prediction of cuttings concentration for enhancing hole cleaning efficiency in eccentric and deviated wells.

Scientific reports·2025
Same author

New recycled polyethylene terephthalate imidazolium ionic liquids and their applications for co<sub>2</sub> exhaust capture.

Scientific reports·2025
Same author

Experimental and theoretical investigation of cationic-based fluorescent-tagged polyacrylate copolymers for improving oil recovery.

Scientific reports·2024
Same author

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions.

Scientific reports·2024

Related Experiment Video

Updated: May 3, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

2.6K

A hybrid simulation-machine learning proxy model for waterflood design optimization in the Bahariya Formation.

Ramy Gad1, Adel M Salem2,3, Omar M El Farouk4

  • 1Department of Petroleum Engineering, Petrosannan Petroleum Company, Cairo, Egypt. rmgg1@pme.suezuni.edu.eg.

Scientific Reports
|May 1, 2026
PubMed
Summary

This study combines machine learning and reservoir simulation to optimize waterflooding in Egypt's Bahariya Formation. Peripheral flooding yielded the highest oil recovery, with AI models accurately predicting performance and identifying key influencing factors for improved field development.

Keywords:
Hybrid modelingMachine learningRecovery factor predictionReservoir managementWaterflooding optimization

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.6K
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

11.8K

Related Experiment Videos

Last Updated: May 3, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

2.6K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.6K
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

11.8K

Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence in Energy
  • Reservoir Management

Background:

  • Optimizing waterflooding is crucial for maximizing oil recovery in complex geological formations.
  • Accurate prediction of oil recovery efficiency (RF%) is essential for effective reservoir development planning.
  • Conventional simulation methods can be time-intensive for screening multiple waterflooding scenarios.

Purpose of the Study:

  • To develop and validate a hybrid methodology combining machine learning (ML) and reservoir simulation for waterflooding optimization.
  • To accurately predict oil recovery efficiency (RF%) across different injection patterns in the Bahariya Formation.
  • To identify dominant reservoir parameters influencing oil recovery for various waterflooding strategies.

Main Methods:

  • A hybrid approach integrating ML models (e.g., linear regression) with conventional reservoir simulation.
  • Development of data-driven models to predict Ultimate Oil Recovery (UOR) for Peripheral, Staggered Line Drive (SLD), and 5-Spot injection patterns.
  • Utilizing permutation importance analysis to quantify the influence of key reservoir parameters on recovery.

Main Results:

  • Peripheral flooding demonstrated the highest UOR (44.7%), followed by SLD (39.4%) and 5-Spot (33.7%).
  • ML models achieved high predictive accuracy with R² scores > 0.95 and low RMSE values.
  • Residual oil saturation (Sor) was the most significant parameter (38-42%), with injection rate (WINJ) and API gravity showing pattern-dependent importance.

Conclusions:

  • The hybrid AI-numerical framework offers an efficient method for rapid waterflooding scenario screening and optimization.
  • Optimal waterflooding pattern selection is highly dependent on specific reservoir characteristics.
  • Findings provide practical insights for field engineers to enhance injection strategies and increase recovery factors.