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Related Concept Videos

Design Example: Flow of Oil Through Circular Pipes01:25

Design Example: Flow of Oil Through Circular Pipes

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Understanding fluid flow behavior through pipes is critical in fluid mechanics, especially in applications like oil transportation through pipelines. Hagen-Poiseuille's law provides an exact solution derived from the Navier-Stokes equations for steady, incompressible, and laminar flow within a circular pipe. Hagen-Poiseuille's law helps determine the necessary pressure drop across a pipeline section by determining parameters like pipe length, radius, oil viscosity, and the desired...
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Multiple Pipe Systems01:21

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Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
Series Configuration
In a series configuration, fluid flows sequentially from one pipe...
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Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

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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...
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Plane Potential Flows01:23

Plane Potential Flows

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Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
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Major Losses in Pipes01:28

Major Losses in Pipes

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When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
Fluid flow can be classified as laminar or turbulent, primarily based on the Reynolds number. This dimensionless number reflects the relative influence of inertial to...
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  11. A Novel Neural Network-based Framework To Estimate Oil And Gas Pipelines Life With Missing Input Parameters.
  1. Home
  3. Research Domains
  5. Engineering
  7. Environmental Engineering
  9. Air Pollution Modelling And Control
  11. A Novel Neural Network-based Framework To Estimate Oil And Gas Pipelines Life With Missing Input Parameters.

Related Experiment Video

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters.

Nagoor Basha Shaik1,2, Kittiphong Jongkittinarukorn3, Watit Benjapolakul2

  • 1Department of Mining and Petroleum Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.

Scientific Reports
|February 24, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel neural network model to predict dry gas pipeline life and metal loss, even with missing data. The intelligent model enhances safety and reliability in the oil and gas sector.

Keywords:
Artificial neural networksCorrelation analysisMissing dataOil and gas

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Area of Science:

  • Engineering
  • Data Science

Background:

  • Dry gas pipelines face operational, technical, and environmental challenges like corrosion and leaks.
  • Proactive maintenance and advanced technology are crucial for pipeline safety, reliability, and efficiency.

Purpose of the Study:

  • To develop a novel neural network model for predicting the lifespan of dry gas pipeline systems.
  • To detect metal loss dimension classification in harsh environments, even with missing data.
  • To enhance pipeline health monitoring and management in the oil and gas industry.

Main Methods:

  • A Bayesian regularization-based neural network framework was employed.
  • The model integrates deep learning with industry-specific expertise to handle missing data.
  • The model was trained and validated on four dry gas pipeline datasets.
Pipelines
Reliability

Main Results:

  • The model accurately predicts pipeline health and metal loss dimension class, even with missing input parameters.
  • Achieved satisfactory numerical performance with Mean Squared Error (MSE) values near 0 and R-squared (R²) values near 1.
  • Demonstrated the model's capability to forecast missing data and predict pipeline life using imputed variables.

Conclusions:

  • The proposed intelligent model shows significant potential for real-world application in the oil and gas sector for pipeline health estimation.
  • The framework improves interpretability and reliability through multi-model comparative and sensitivity analyses.
  • The model can aid business planning by reducing accident risks and environmental harm, enhancing overall safety and reliability.