Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Design Example: Alignment of a Road Line Using GIS

Design Example: Alignment of a Road Line Using GIS

The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...

Electronic Distance Measuring Instruments

Electronic Distance Measuring Instruments

Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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

Urinothorax and pleural fluid creatinine: a retrospective analysis of pleural fluid studies at a tertiary care center and a review of the literature.

Journal of thoracic disease·2025

Same author

Correction: Compliance and yield of follow-up colonoscopy after a positive FIT in real-life practice: a university hospital experience.

BMC gastroenterology·2025

Same author

Compliance and yield of follow-up colonoscopy after a positive FIT in real-life practice: a university hospital experience.

BMC gastroenterology·2025

Same author

Prevalence and Characteristics of Itch in Patients with Parkinson's Disease.

Acta dermato-venereologica·2025

Same author

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

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: Jun 8, 2025

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

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

Published on: January 5, 2024

A supervised machine learning model to select a cost-effective directional drilling tool.

Muhammad Nour¹, Said K Elsayed¹, Omar Mahmoud²

¹Department of Petroleum Engineering, Faculty of Petroleum and Mining Engineering, Suez University, Suez, 11252, Egypt.

Scientific Reports

|November 4, 2024

Summary

Optimizing directional drilling tool selection using machine learning can significantly lower field development costs. The XGBoost model accurately predicts section time and cost, accounting for well-specific factors to reduce human bias.

Keywords:

Digital drilling Directional drilling Machine learning Mud motor RSS XGBoost regression

More Related Videos

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

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

Published on: June 1, 2022

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Jun 8, 2025

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

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

Published on: January 5, 2024

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

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

Published on: June 1, 2022

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Petroleum Engineering
Data Science
Machine Learning

Background:

Directional drilling is crucial in the oil and gas industry, necessitating efficient planning and operational optimization.
Selecting the appropriate directional drilling tool, such as Rotary Steerable Systems (RSS) or Positive Displacement Motors (PDM), is key for cost-effectiveness.

Purpose of the Study:

To develop and validate machine learning (ML) models for automating the optimal selection of directional drilling tools.
To predict drilling section time and cost for new wells based on historical offset well data.

Main Methods:

Utilized historical offset well data including lithology, directional, drilling performance, tripping, and casing running information.
Developed and tested various ML algorithms, with XGBoost identified as the most accurate predictor.
The model was designed to account for variations in formation thickness and drilling environments.

Main Results:

The XGBoost ML model demonstrated superior accuracy in predicting section time and cost compared to other algorithms.
The model successfully adjusted tool recommendations based on specific well factors, indicating no universal preference for RSS or PDM.
The data-driven approach effectively reduced human bias in decision-making.

Conclusions:

Machine learning provides a robust, data-driven method for optimizing directional drilling tool selection.
Tool selection is highly dependent on well-specific geological and operational factors, not a one-size-fits-all solution.
Implementing this approach can lead to significant reductions in field development costs, especially in extensive drilling campaigns.