Evaluation of Machine Learning-Based Regression Techniques for Prediction of Oil and Gas Pipelines Defect

Aldosari, Huda; Elfouly, Raafat; Ammar, Reda A.

doi:10.1109/csci51800.2020.00271

Cited by 11 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous studies have explored and documented AI's effectiveness in modelling O&G over the last three years. Many initial efforts comprised basic and conventional AI techniques, including perceptron-based Artificial Neural Network (ANN) [34], [35], [36].…”

Section: The Distribution Of Predictive Analytics In Oandg Fieldmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

View full text Add to dashboard Cite

Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

show abstract

Section: The Distribution Of Predictive Analytics In Oandg Fieldmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Numerous studies have explored and documented AI's effectiveness in modeling O&G over the last three years. Many initial efforts comprised basic and conventional AI techniques, including perceptron-based Artificial Neural Networks (ANNs) [37][38][39]. Predicting the performance and production of O&G has consistently presented a challenge.…”

Section: Classification 53%mentioning

confidence: 99%

A Review of Predictive Analytics Models in the Oil and Gas Industries

R Azmi,

Yusoff,

Mohd Sallehud-din

2024

Sensors

View full text Add to dashboard Cite

Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.

show abstract

“…Pipelines are not harmed during ILI inspections. Therefore, these technologies are referred to as nondestructive testing (NDT) or non-destructive inspection (NDI) technologies [5]. Numerous ILI sensor technologies, including magnetic flux leakage (MFL) sensors [6], ultra-sonic sensors (UTs) [7], electro-magnetic acoustic transducers (EMATs) [8], and laser profilometers (LPs) [9], are used to detect faults during in-line inspections [10].…”

Section: Introductionmentioning

confidence: 99%

Long-Range Wireless Communication for In-Line Inspection Robot: 2.4 km On-Site Test

et al. 2023

View full text Add to dashboard Cite

This paper presents a study of the feasibility of using in-line inspection (ILI) techniques with long-range communication-capable robotic systems deployed with advanced inspection sensors in natural gas distribution pipelines, which are rare in the literature. The study involved selecting appropriate antennas and determining the appropriate communication frequency for an ILI robot operating on Istanbul 12″ and 16″ steel pipelines. The paper identifies the frequency windows with low losses, presents received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) information for various scenarios, and evaluates the impact of T-junctions, which are known to be the worst components in terms of communication. To utilize the pipeline as a waveguide, low-attenuation-frequency windows were determined, which improved communication by a factor of 500 compared to aerial communication. The results of laboratory tests on a 50 m pipeline and real-world tests on a 2.4 km pipeline indicate that long-distance communication and video transmission are possible at frequencies of around 917 MHz with low-gain antennas. The study also assessed the impact of the early diagnosis of anomalies without incidents on the environment, achievable with ILI robots using long-range wireless communication.

show abstract

Evaluation of Machine Learning-Based Regression Techniques for Prediction of Oil and Gas Pipelines Defect

Cited by 11 publications

References 16 publications

Predictive Analytics Performance on Oil and Gas: A Significant Review

Predictive Analytics Performance on Oil and Gas: A Significant Review

A Review of Predictive Analytics Models in the Oil and Gas Industries

Long-Range Wireless Communication for In-Line Inspection Robot: 2.4 km On-Site Test

Contact Info

Product

Resources

About