黑料网

ISSN 2472-0518

Oil & Gas Research
黑料网

Our Group organises 3000+ Global Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ 黑料网 Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

黑料网 Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
  • Review Article   
  • Oil Gas Res,
  • DOI: 10.4172/2472-0518.1000282

Current Overview and way forward for the use of Machine Learning in the Field of Petroleum Gas Hydrates

Negi BS*
O. P. Jindal Global University, Sonepat, Haryana-131001, India
*Corresponding Author : Negi BS, O. P. Jindal Global University, Sonepat, Haryana-131001, India, Email: Negi.bs@gmail.com

Received Date: Jan 02, 2023 / Published Date: Jan 27, 2023

Abstract

Gas hydrates represent one of the main inflow assurance challenges in the oil painting and gas assiduity as they can lead to plugging of channels and process outfit. In this paper we present a literature study performed to estimate the current state of the use of machine literacy styles within the field of gas hydrates with specific focus on the oil painting chemistry. A common analysis fashion for crude canvases is Fourier transfigures Ion Cyclotron Resonance Mass Spectrometry (FT- ICR MS) which could be a good approach to achieving a better understanding of the chemical composition of hydrates, and the use of machine literacy in the field of FT- ICR MS was thus also examined. Several machine literacy styles were linked as promising, their use in the literature was reviewed and a textbook analysis study was performed to identify the main motifs within the publications. The literature hunt revealed that the publications on the combination of FT- ICR MS, machine literacy and gas hydrates are limited to one. Utmost of the work on gas hydrates is related to thermodynamics, while FT- ICR MS is substantially used for chemical analysis of canvases. Still, with the combination of FT- ICR MS and machine literacy to estimate samples related to gas hydrates; it could be possible to ameliorate the understanding of the composition of hydrates and thereby identify hydrate active composites responsible for the differences between canvases forming plugging hydrates and canvases forming transmittable hydrates.

Citation: Negi BS (2023) Current Overview and way forward for the use of Machine Learning in the Field of Petroleum Gas Hydrates. Oil Gas Res 9: 281. Doi: 10.4172/2472-0518.1000282

Copyright: © 2023 Negi BS. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

International Conferences 2024-25
 
Meet Inspiring Speakers and Experts at our 3000+ Global

Conferences by Country

Medical & Clinical Conferences

Conferences By Subject

Top