Intelligent prediction of methane hydrate phase boundary conditions in ionic liquids using deep learning algorithms-Reference-Cited by-同舟云学术

Intelligent prediction of methane hydrate phase boundary conditions in ionic liquids using deep learning algorithms

Published:2023-02-17 Issue: Volume: Page:1-20
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Bavoh Cornelius Borecho¹²³,Sambo Chico⁴,Quainoo Ato Kwamena⁵,Lal Bhajan¹²

Affiliation:

1. Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia

2. Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia

3. Chemical and Petrochemical Engineering Department, University of Mines and Technology, Tarkwa, Ghana

4. Petroleum Engineering Department, University of Louisiana at Lafayette, Lafayette, Louisiana, USA

5. Geological Science and Geology and Petroleum Engineering Department, Missouri University of Science and Technology, Rolla, Missouri, USA

Publisher

Informa UK Limited

Subject

Geotechnical Engineering and Engineering Geology,Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2023.2175865

Reference46 articles.

1. Toward reliable model for prediction Drilling Fluid Density at wellbore conditions: A LSSVM model

2. A LSSVM approach for determining well placement and conning phenomena in horizontal wells

3. Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs

4. Machine learning-based models for predicting permeability impairment due to scale deposition

5. Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach

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