Data-driven estimation for permeability of simplex pore-throat reservoirs via an improved light gradient boosting machine: A demonstration of sand-mud profile, Ordos Basin, northern China-Reference-Cited by-同舟云学术

Data-driven estimation for permeability of simplex pore-throat reservoirs via an improved light gradient boosting machine: A demonstration of sand-mud profile, Ordos Basin, northern China

Published:2022-10 Issue: Volume:217 Page:110909
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Gu Yufeng,Yang Yang,Gao Yinshan,Yan Shiliang,Zhang Daoyong,Zhang Chen

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference50 articles.

1. Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization;Ahmadi;Geophys. Prospect.,2013

2. A hybrid particle swarm optimization and support vector regression model for modeling permeability prediction of hydrocarbon reservoir;Akande;J. Petrol. Sci. Eng.,2016

3. Tectonic significance of porosity and permeability regimes in the red beds formations of the south Georgia Rift Basin;Akintunde;Tectonophysics,2014

4. Enhanced reservoir description: using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/wells;Amaefule,1993

5. Fluid flow through granular beds;Carman;Trans. Inst. Chem. Eng.,1937

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An interpretable ensemble machine-learning workflow for permeability predictions in tight sandstone reservoirs using logging data;GEOPHYSICS;2024-08-12

2. Modeling CO2 solubility in water using gradient boosting and light gradient boosting machine;Scientific Reports;2024-06-12

3. Modeling thermo-physical properties of hydrogen utilizing machine learning schemes: Viscosity, density, diffusivity, and thermal conductivity;International Journal of Hydrogen Energy;2024-06

4. Employing ensemble learning techniques for modeling nanofluids' specific heat capacity;International Communications in Heat and Mass Transfer;2023-04

5. Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery;Separation and Purification Technology;2023-04